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Last Updated :2024/09/03

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Master

Affiliation (Master)

  • Faculty of Information Science and Technology　Media and Network Technologies　Information Media Science and Technology

Affiliation (Master)

  • Faculty of Information Science and Technology　Media and Network Technologies　Information Media Science and Technology

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Profile and Settings

Degree

  • PhD (Engineering)（2004/03 Hokkaido University）

Profile and Settings

  • Profile

    I am working on automatic text understanding algorithms to allow machines analyze average human behavior. My main research topics are common sense knowledge acquisition, affect processing and machine ethics, however I am also involved in artificial humor, metaphors understanding and generation, cyber-bullying detection and many more (especially cross-disciplinary ones).

  • Name (Japanese)

    RZEPKA

  • Name (Kana)

    Rafal

  • Name

    200901069857545387

Alternate Names

Achievement

Research Interests

  • Moral Machines   Artificial Intelligence   Artificial General Intelligence   Natural Language Processing   Common Sense Knowledge   Affective Computing   Machine Ethics   machine intelligence   

Research Areas

  • Informatics / Intelligent informatics / Knowledge Acquisition
  • Informatics / Sensitivity (kansei) informatics / sentiment analysis
  • Humanities & social sciences / Philosophy and ethics
  • Humanities & social sciences / Cognitive sciences
  • Informatics / Robotics and intelligent systems
  • Informatics / Learning support systems

Research Experience

  • 2024/09 - Today Hokkaido University Faculty of Information Science and Technology Associate Professor
  • 2007/04 - 2024/08 Hokkaido University Faculty of Information Science and Technology Assistant Professor
  • 2017/06 - 2020/03 RIKEN Center for Advanced Intelligence Project (AIP) Visiting Researcher
  • 2019/02 - 2019/03 Queensland University of Technology Digital Media Research Center
  • 2017/01 - 2017/09 Australian National University Centre of Excellence for the Dynamic of Language Visiting Fellow
  • 2013/02 - 2013/03 Monash University Behavioural Studies Visiting Scholar
  • 2011/02 - 2011/03 Stanford University The Center for the Study of Language and Information (CSLI)
  • 2004/04 - 2007/03 Hokkaido University Graduate School of Information Science and Technology Assistant

Education

  • 2001/04 - 2004/03  Hokkaido University  Faculty of Engineering  Division of Electrical, Electronic and Information Engineering
  • 1993/10 - 1999/06  Adam Mickiewicz University  Department of Neophilology

Committee Memberships

  • 2023/07 - Today   EMNLP   Ethics Committee Member
  • 2023/06 - Today   Japanese Society of Artificial Intelligence   Board of Representatives
  • 2021 - Today   Information Processing and Management Journal Program Committee Member
  • 2019 - Today   New Generation Computing Journal Program Committee Member
  • 2022/12 -2024/03   MDPI   Guest Editor of Applied Sciences Journal Special Issue "Application of Artificial Intelligence Methods in Processing of Emotions, Decisions and Opinions"
  • 2021/06 -2023/06   Japanese Society of Artificial Intelligence   Board of Trustees Member
  • 2022/04 -2022/10   Main Organizer - The 8th Linguistic and Cognitive Approaches To Dialog Agents Workshop - LaCATODA 2022
  • 2020/10 -2021/10   "Recent Developments in Creative Language Processing" MDPI Special Issue   Guest Editor
  • 2021/04 -2021/09   IJCAI 2021 Workshops   LaCATODA Workshop Organizer
  • 2021/01 -2021/08   Main Organizer - The 7th Linguistic and Cognitive Approaches To Dialog Agents Workshop - LaCATODA 2021   Rafal Rzepka;Jordi Vallverdu;Andre Wlodarczyk;Michal Ptaszynski;Pawel Dybala
  • 2021/01 -2021/06   JSAI 2021   International Session Co-organizer
  • 2021/01   "Ethical NLP" Special Issue of IPM Journal   Guest Chief Editor
  • 2020/04 -2020/08   IJCAI 2020 Workshops   LaCATODA Workshop Organizer
  • 2020 -2020   IJCAI 2020 Program Committee Member
  • 2020 -2020   AACL-IJCNLP 2020 Program Committee Member
  • 2020 -2020   COLING 2020 Program Committee Member
  • 2020 -2020   HAI 2020 Program Committee Member
  • 2020 -2020   KBS Special Issue on Commonsense Knowledge Representation & Reasoning Guest Editor
  • 2019/03 -2019/08   IJCAI 2019 Workshops   LaCATODA Workshop Organizer
  • 2019 -2019   iCast 2019 Program Committee Member
  • 2019 -2019   Artificial Intelligence and Cognition (AIC 2019) Program Committee Member
  • 2019 -2019   Paladyn - Journal of Behavioral Robotics Program Committee Member
  • 2019 -2019   Symposium on Analytics-based (Cognitively-enabled) Social Systems Program Committee
  • Cambride Core Experimental Results   Program Committee Member
  • Information Processing Society of Japan   Student Member   Information Processing Society of Japan
  • The Institute of Electronics, Information and Communication Engineers   Student Member   The Institute of Electronics, Information and Communication Engineers
  • Japanese Cognitive Science Society   Student Member   Japanese Cognitive Science Society
  • Japanese Society for Artificial Intelligence   Student Member   Japanese Society for Artificial Intelligence
  • The Institute of Electrical and Electronics Engineering (IEEE)   Student Member   The Institute of Electrical and Electronics Engineering (IEEE)

Awards

  • 2023/04 LTC 2023 Conference Best Paper Award
     Sentiment Analysis of Polish Online News Covering Controversial Topics - Comparison Between Lexicon and Statistical Approaches 
    受賞者: Joanna Szwoch;Mateusz Staszkow;Rafal Rzepka;Kenji Araki
  • 2021/12 ことば工学研究会 優秀賞
     論文の構造とタイトルを独立して考慮した医学論文検索モデルの提案 
    受賞者: 吉井 瑞貴、竹下 昌宏、ジェプカ・ラファウ、荒木 健治
  • 2021/11 電気・情報関係学会北海道支部 若手優秀論文発表賞
     BM25Tを用いたオンライン上の医療ニュース記事の根拠論文候補抽出 
    受賞者: 吉井 瑞貴, 竹下 昌志, ジェプカ ラファウ, 荒木 健治
  • 2020 WI2 Budding Research Award
     Mapping arguments to key point: Match Scoring of arguments using sentence embedding and MoverScore without labelled data 
    受賞者: Daiki Shirafuji;Rafal Rzepka;Kenji Araki
  • 2020 WI2 Budding Research Award
     Unsupervised summarization of arguments toward key point generation with Sentence-BERT-based method 
    受賞者: Daiki Shirafuji, Rafal Rzepka, Kenji Araki
  • 2019 BICA Outstanding Research Award
     "Emotional and Moral Impressions Associated with Buddhist Religious Terms in Japanese Blogs - Preliminary Analysis" 
    受賞者: Jagna Nieuważny;Fumito Masui;Michal Ptaszynski;Kenji Araki;Rafal Rzepka;Karol Nowakowski
  • 2019 BICA Best Innovative Work Award
     "Emotional and Moral Impressions Associated with Buddhist Religious Terms in Japanese Blogs - Preliminary Analysis" 
    受賞者: Jagna Nieuważny, Fumito Masui, Michal Ptaszynski, Kenji Araki, Rafal Rzepka, Karol Nowakowski
  • 2015 "Emotions, Decisions, Opinions" EDO 2015 Best Paper Award
     Exploiting Wikipedia-based Information-rich Taxonomy for Extracting Location and Creator Related Information for ConceptNet Expansion
  • 2011 Pacific Association For Computational Linguistics Best Paper Award
     Introducing Grammatically Aware Regular Expressions
  • 2010 Japan Society for Fuzzy Theory and Intelligent Informatics Encouragement Award
     Teaching a Humanoid Robot through Physical Feedback: So Easy Even a Five Year Old Could Use It
  • 2008 Second International Conference on Kansei Engineering & Affective Systems Best Paper Award
     Disentangling emotions from the Web. Internet in the service of affect analysis
  • 2002/10 IEEE Hokkaido Chapter Best Paper Award
     Prediction of the User’s Reply Using Emotional Information Retrieved from Internet Resources
  • 1996 Japanese Ministry of Education Scolarship
     
    1996, 1999

Published Papers

  • Rafal Rzepka
    人工知能 日本人工知能学会 38 (6) 943 - 953 2023/12 [Not refereed][Invited]
  • Dušan Radisavljević, Rafal Rzepka, Kenji Araki
    Applied Sciences 13 (7) 4506 - 4506 2023/04/02 
    The popularity of social media services has led to an increase of personality-relevant data in online spaces. While the majority of people who use these services tend to express their personality through measures offered by the Myers–Briggs Type Indicator (MBTI), another personality model known as the Big Five has been a dominant paradigm in academic works that deal with personality research. In this paper, we seek to bridge the gap between the MBTI, Big Five and another personality model known as the Enneagram of Personality, with the goal of increasing the amount of resources for the Big Five model. We further explore the relationship that was previously reported between the MBTI types and certain Big Five traits as well as test for the presence of a similar relationship between Enneagram and Big Five measures. We propose a new method relying on psycholingusitc features selected based on their relationship with the MBTI model. This approach showed the best performance through our experiments and led to an increase of up to 3% in automatic personality recognition for Big Five traits on the per-trait level. Our detailed experimentation offers further insight into the nature of personality and into how well it translates between different personality models.
  • Utilizing BERT with Auxiliary Sentences Generation to Improve Accuracy of Japanese Aspect-based Sentiment Analysis Task
    Yiyang Zhang, Masashi Takeshita, Rafal Rzepka, Kenji Araki
    In the proceedings of the Language Technology Conference (LTC’23) 348 - 352 2023/04 [Refereed][Not invited]
  • Sentiment Analysis of Polish Online News Covering Controversial Topics – Comparison Between Lexicon and Statistical Approaches
    Joanna Szwoch, Mateusz Staszkow, Rafal Rzepka, Kenji Araki
    In the proceedings of the Language Technology Conference (LTC’23) 277 - 281 2023/04 [Refereed][Not invited]
  • Improving Performance of Affect Analysis System by Expanding Affect Lexicon
    Lu Wang, Michal Ptaszynski, Pawel Dybala, Yuki Urabe, Rafal Rzepka, Fumito Masui
    In the proceedings of the Language Technology Conference (LTC’23) 314 - 319 2023/04 [Refereed]
  • Utilizing Wikipedia for Retrieving Synonyms of Trade Security-related Technical Terms
    Rafal Rzepka, Shinji Muraji, Akihiko Obayashi
    In the proceedings of the Language Technology Conference (LTC’23) 250 - 254 2023/04 [Refereed][Not invited]
  • Xiaodong Liu, Rafal Rzepka, Kenji Araki
    Natural Language Engineering 1 - 31 1351-3249 2022/12/19 
    Abstract There are many types of approaches for Paraphrase Identification (PI), an NLP task of determining whether a sentence pair has equivalent semantics. Traditional approaches mainly consist of unsupervised learning and feature engineering, which are computationally inexpensive. However, their task performance is moderate nowadays. To seek a method that can preserve the low computational costs of traditional approaches but yield better task performance, we take an investigation into neural network-based transfer learning approaches. We discover that by improving the usage of parameters efficiently for feature-based transfer, our research goal can be accomplished. Regarding the improvement, we propose a pre-trained task-specific architecture. The fixed parameters of the pre-trained architecture can be shared by multiple classifiers with small additional parameters. As a result, the computational cost left involving parameter update is only generated from classifier-tuning: the features output from the architecture combined with lexical overlap features are fed into a single classifier for tuning. Furthermore, the pre-trained task-specific architecture can be applied to natural language inference and semantic textual similarity tasks as well. Such technical novelty leads to slight consumption of computational and memory resources for each task and is also conducive to power-efficient continual learning. The experimental results show that our proposed method is competitive with adapter-BERT (a parameter-efficient fine-tuning approach) over some tasks while consuming only 16% trainable parameters and saving 69-96% time for parameter update.
  • Using Convolutional Neural Network for improving inference of interrogative sentences in a dialogue system
    Kei Kawai, Rafal Rzepka, Tatsuki Nemoto
    IEEE Proceedings of ACII 2022 Workshops, The Eighth Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2022) 2022/10 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2022/09 [Refereed]
  • Expanding Export Control-related Data for Expert System
    Akihiko Obayashi, Rafal Rzepka
    Proceedings of 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems 2022/09 [Refereed][Not invited]
  • Creation of Polish Online News Corpus for Political Polarization Studies
    Joanna Szwoch, Mateusz Staszkow, Rafal Rzepka, Kenji Araki
    Proceedings of The First LREC 2022 workshop on Natural Language Processing for Political Sciences 86 - 90 2022/06 [Refereed][Not invited]
  • Pitfalls of Current AI Helping To Analyze Japanese Tweets
    Rafal Rzepka
    Book of abstracts of the Practicing Japan – 35 years of Japanese Studies in Poznań and Kraków conference 2022/03
  • Comparison of Zero-Shot Ethical Classification With and Without Automatically Generated Consequences
    Rafał Rzepka, Kenji Araki
    Proceedings of the AAAI Spring Symposium on Approaches to Ethical Computing Metrics for Measuring AI’s Proficiency and Competency for Ethical Reasoning 2022/03 [Refereed][Not invited]
  • Mateusz Babieno, Masashi Takeshita, Dušan Radisavljević, Rafal Rzepka, Kenji Araki
    Applied Sciences 12 (4) 2081 - 2081 2022/02/17 [Refereed]
  • Rafal Rzepka
    Advances in Intelligent Systems and Computing 2022
  • Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba, Fumito Masui, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    CoRR abs/2203.02116 2022
  • Radisavljevi?, D., Batalo, B., Rzepka, R., Araki, K.
    Lecture Notes in Networks and Systems 296 2367-3389 2022 [Refereed][Not invited]
  • Hiyori Yoshikawa, Tomoya Iwakura, Kimi Kaneko, Hiroaki Yoshida, Yasutaka Kumano, Kazutaka Shimada, Rafal Rzepka, Patrycja Swieczkowska
    the Proceedings fo Recent Advances in Natural Language Processing (RANLP 2021) 1575 - 1585 2021/09 [Refereed][Not invited]
  • Annotated Question and Answer Dataset for Security Export Control
    Akihiko Obayashi, Rafal Rzepka
    Proceedings of The 7th Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2021) IJCAI 2021 Workshop, CEUR Workshop Proceedings vol. 2935 2021/08 [Refereed][Not invited]
  • Current Language Models Might Not Be Suitable For Reverse Engineering Moral Wisdom of Crowds
    Rafal Rzepka, Yuki Katsumata, Kenji Araki
    Engineering and Reverse-Engineering Morality Workshop at CogSci 2021 2021/07
  • Daiki Shirafuji, Rafal Rzepka, Kenji Araki
    IEEE Access 9 103091 - 103109 2169-3536 2021/05 [Refereed][Not invited]
  • Jagna Nieuważny, Karol Nowakowski, Michal Ptaszyński, Fumito Masui, Rafal Rzepka, Kenji Araki
    Cognitive Systems Research 66 89 - 99 1389-0417 2021/03 [Refereed][Not invited]
  • Rafal Rzepka
    Procedia Computer Science 192 2709 - 2719 1877-0509 2021 [Refereed]
  • Rafal Rzepka
    Information Processing & Management 58 (1) 102414 - 102414 0306-4573 2021/01 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2020/12 [Refereed]
  • Can Existing Methods Debias Languages Other than English? First Attempt to Analyze and Mitigate Japanese Word Embeddings
    Masashi Takeshita, Yuki Katsumata, Rafal Rzepka, Kenji Araki
    Proceedings of COLING 2020 Workshop on Gender Bias in Natural Language Processing 2020/12 [Refereed]
  • Da Li, Rafal Rzepka, Michal Ptaszynski, Kenji Araki
    Information Processing & Management 57 (6) 102290 - 102290 0306-4573 2020/11 [Refereed]
  • Maria Skeppstedt, Magnus Ahltorp, Kostiantyn Kucher, Andreas Kerren, Rafal Rzepka, Kenji Araki
    Selected Papers from the CLARIN Annual Conference 2019, Linköping Electronic Conference Proceedings 145 - 156 2020/07/03 [Refereed]
  • Rafal Rzepka
    Journal of Advanced Computational Intelligence and Intelligent Informatics 24 (1) 156 - 168 1883-8014 2020/01/20 [Refereed]
     
    There is little research into designing artificial motivational agents. The end-goal of our studies is therefore to create a dialogue system that would motivate users to do their everyday tasks using natural language. In this paper, we present a method of distinguishing texts containing motivational advice from regular texts to sort out noise in training data for our dialogue system. We implemented a novel method of chaining two shallow networks together by utilizing the output results of the first network to determine the input for the second one. We achieved F-score of 0.94 and 0.97 with our proposed method. The contributions of this paper are threefold: first, we successfully identified 14 hand-crafted features that make a text motivational/advisory. Secondly, we were able to create a classifying algorithm that distinguishes motivational/advisory texts from regular ones. Finally, our proposed method can be applied to other text classification tasks.
  • Rafal Rzepka
    Communications in Computer and Information Science 2020
  • Babieno, M., Rzepka, R., Araki, K.
    Communications in Computer and Information Science 1215 CCIS 1865-0937 2020
  • Babieno, M., Rzepka, R., Araki, K., Dybala, P.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12158 LNAI 1611-3349 2020
  • Mateusz Babieno, Rafal Rzepka, Kenji Araki
    Proceedings of the 16th International Conference of the Pacific Association for Computational Linguistics, PACLING 2019 2020 [Refereed]
  • Jagna Nieuważny, Fumito Masui, Michal Ptaszynski, Rafal Rzepka, Karol Nowakowski
    Cognitive Systems Research 59 329 - 344 1389-0417 2020/01 [Refereed]
  • Analysis of Human Thoughts in the Big Data Era: A Case Study of Showerthought Sub-Reddit
    Siaw-Fong Chung, Rafal Rzepka
    Proceedings of 10th Conference on Digital Archives and Digital Humanities 2019/12 [Refereed]
  • Creating Reverse Dictionary of English Idiomatic Expressions by Mapping Word Embeddings to Singular Vectors
    Xiaodong Liu, Rafal Rzepka, Kenji Araki
    Technical Report of JSAI Special Interest Group for Interactive Information Access and Visualization 2019/11 [Not refereed]
  • Visualising and evaluating the effects of combining active learning with word embedding features
    Maria Skeppstedt, Rafal Rzepka, Kenji Araki, Andreas Kerren
    Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019) 2019/10 [Refereed]
  • Application of a Topic Model Visualisation Tool to a Second Language
    Maria Skeppstedt, Magnus Ahltorp, Andreas Kerren, Rafal Rzepka, Kenji Araki
    Book of Abstracts of the CLARIN Annual Conference 2019 2019/10 [Refereed]
  • Towards Interactive Advisory System for Security Export Control
    Akihiko Obayashi, Rafal Rzepka
    Proceedings of the IJCAI Workshop on Language Sense on Computer 2019/08 [Refereed]
  • Comparing Conceptual Metaphor Theory-Related Features in Searching for Figurative Expressions in Japanese Literary Texts
    Mateusz Babieno, Rafal Rzepka, Kenji Araki
    IJCAI Workshop on Language Sense on Computer 2019/08 [Refereed]
  • A Convolutional Neural Network For Ranking Advice Quality In Texts For A Motivational Dialogue System
    Patrycja Swieczkowska, Rafal Rzepka, Kenji Araki
    Proceedings of the IJCAI Workshop on Linguistic and Cognitive Approaches To Dialog Agents Workshop 2019/08 [Refereed]
  • Debate Outcome Prediction using Automatic Persuasiveness Evaluation and Counterargument Relations
    Daiki Shirafuji, Rafal Rzepka, Kenji Araki
    IJCAI Workshop on Linguistic and Cognitive Approaches To Dialog Agents Workshop 2019/08 [Refereed]
  • Da Li, Rafal Rzepka, Michal Ptaszynski, Kenji Araki
    IJCAI Workshop on Linguistic and Cognitive Approaches To Dialog Agents Workshop 11 - 18 2019/08 [Refereed]
  • Implicit Knowledge Completion Using Relevance Calculation of Distributed Word Representations
    Sho Takishita, Rafal Rzepka, Kenji Araki
    Proceedings of the IJCAI Workshop on Bridging The Gap Between Human and Automated Reasoning 2019/08 [Refereed]
  • Jagna Nieuwazny, Michal Ptaszynski, Karol Nowakowski, Fumito Masui, Rafał Rzepka, Kenji Araki
    Proceedings of BICA 2019 387 - 392 2019/08
  • LI Da, RZEPKA Rafal, PTASZYNSKI Michal, ARAKI Kenji
    Proceedings of the Annual Conference of JSAI 一般社団法人 人工知能学会 2E4-OS-9-04 2E4OS904 - 2E4OS904 2019/06 [Not refereed]
     
    Nowadays, social media have become the essential part of our lives. Internet slang is an informal language used in everyday online communication which quickly becomes adopted or discarded by new generations. Similarly, pictograms (emoticons/emojis) have been widely used in social media as a mean for graphical expression of emotions. People can convey delicate nuances through textual information when supported with emoticons, and the effectiveness of computer-mediated communication is also improved. Therefore, it is important to fully understand the influence of Internet slang and emoticons on social media. In this paper, we propose a convolutional neural network model considering Internet slang and emoticons for sentiment analysis of Weibo which is the most popular Chinese social media platform. Our experimental results show that the proposed method can significantly improve the performance for predicting sentiment polarity.
  • STARS at the NTCIR-14 QA Lab-PoliInfo Classification Task
    Daiki Shirafuji, Sho Takishita, Patrycja Swieczkowska, Rafal Rzepka, Kenji Araki
    Proceedings of the 14th NTCIR Conference on Evaluation of Information Access Technology 2019/06 [Refereed]
  • Rafal Rzepka
    FOURTH IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, ROBOT, VOL 2 848 763 - 768 2194-5357 2019 [Refereed][Not invited]
  • Da Li, Rafal Rzepka, Michal Ptaszynski, Kenji Araki
    Proceedings of the 2nd Workshop on Affective Content Analysis (AffCon 2019) co-located with Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)(AffCon@AAAI) 88 - 103 2019
  • Michal Ptaszynski, Pawel Lempa, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki, Michal Wroczynski, Gniewosz Leliwa
    Journal of the Association for Information Systems 20 (8) 1075 - 1127 2019 [Refereed]
  • Machine Learning Approach Considering Chinese Slang Lexicon and Emoticons for Chinese Social Media Sentiment Analysis
    Da Li, Rafal Rzepka, Michal Ptaszynski, Kenji Araki
    AAAI-19 Workshop on Affective Content Analysis (AffCon 2019) 2019/01 [Refereed][Not invited]
  • Akinori Abe, Rafal Rzepka, Michal Ptaszynski
    Adv. Hum. Comput. Interact. 2019 3081602 - 2 2019 [Not refereed][Invited]
  • Automatic Extraction of Harmful Sentence Patterns with Application in Cyberbullying Detection
    Michal Ptaszynski, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Human Language Technology. Challenges for Computer Science and Linguistics 349 - 363 2018/11 [Refereed][Invited]
  • Preliminary Statistical Analysis of Emotional and Moral Impressions Associated with Buddhist Religious Terms
    Jagna Nieuwazny, Fumito Masui, Michal Ptaszynski, Rafal Rzepka, Karol Nowakowski
    Proceedings of International Workshop on Modern Science and Technology (IWMST 2018) 2018/10 [Refereed][Not invited]
  • Da Li, Rafal Rzepka, Michal Ptaszynski, Kenji Araki
    Proceedings of The Ninth IEEE International Conference on Awareness Science and Technology (iCAST 2018) 161 - 166 2018/09 [Refereed][Not invited]
  • Analyzing motivating texts for modeling human-like motivation techniques in emotionally intelligent dialogue systems
    Patrycja Swieczkowska, Rafal Rzepka, Kenji Araki
    Proceedings of the Ninth Annual Meeting of the BICA Society (BICA2018) 355 - 360 2018/08 [Refereed][Not invited]
  • Conversational Control Interface to Facilitate Situational Understanding in a City Surveillance Setting
    Daniel Harborne, Dave Braines, Alun Preece, Rafal Rzepka
    Proceedings of Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2018) IJCAI-ECAI 2018 Workshop 59 - 66 2018/07 [Refereed][Not invited]
  • Rafal Rzepka, Mitsuru Takizawa, Jordi Vallverdu, Michal Ptaszynski, Pawel Dybala, Kenji Araki
    International Journal of Computational Linguistics Research 9 (1) 10 - 26 0976-416X 2018/03 [Refereed][Not invited]
     
    This paper summarizes several lexical methods for more comprehensive affect recognition in text using an example of typed utterances. We introduce a set of algorithms that are capable of recognizing emotions of user’s statements in order to achieve more effective and smoother human-machine conversation. Aspects often neglected by existing systems working with Japanese language, e.g. compound sentences, double negation sentences, modifiers as adverbs and emoticons were combined and their higher effectiveness in recognizing affect in more complicated sentences was confirmed through evaluation experiments. The results are introduced together with separate analysis of emoticons’ influence on emotional load. We also discuss importance of predicting human emotions not only in the field of human-computer interaction but also its meaning for developing ethical chatbots.
  • Importance of Contextual Knowledge in Artificial Moral Agents Development
    Rafal Rzepka, Kenji Araki
    AAAI 2018 Spring Symposium on AI and Society: Ethics, Safety and Trustworthiness in Intelligent Agents 61 - 68 2018/03 [Refereed][Not invited]
  • Michal Mazur, Rafal Rzepka, Kenji Araki
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10930 332 - 345 1611-3349 2018 [Refereed][Invited]
     
    One of the essential parts of second language curriculum is teaching vocabulary. Until now many existing techniques tried to facilitate word acquisition, but one method which has been paid less attention to is code-switching. In this paper, we present an experimental system for computer assisted vocabulary learning in context using a code-switching based method, focusing on teaching Japanese vocabulary to foreign language learners. First, we briefly introduce our Co-MIX method for vocabulary teaching systems using code-switching phenomenon to support vocabulary acquisition. Next, we show how we utilize incidental learning technique with graded readers to facilitate vocabulary learning. We present the systems architecture, underlying technologies and the initial evaluation of the system’s performance by using semantic differential scale. Finally, we discuss the evaluation results and compare them with our English vocabulary teaching system.
  • Marek Krawczyk, Rafal Rzepka, Kenji Araki
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10930 262 - 274 1611-3349 2018 [Refereed][Invited]
     
    In this paper we present a method for extracting IsA assertions (hyponymy relations), AtLocation assertions (informing of the location of an object or place), LocatedNear assertions (informing of neighboring locations), CreatedBy assertions (informing of the creator of an object) and MemberOf assertions (informing of group membership) automatically from Japanese Wikipedia XML dump files. We use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce information-rich taxonomy. From this taxonomy we extract additional information, in this case AtLocation, LocatedNear, CreatedBy and MemberOf types of assertions, using our original method. The presented experiments prove that both methods produce satisfactory results: we were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6,217 LocatedNear assertion pairs with 98.5% reliability, 270,230 CreatedBy assertion pairs with 78.5% reliability and 21,053 MemberOf assertions with 87.0% reliability. Our method surpassed the baseline system in terms of both precision and the number of acquired assertions.
  • Rafal Rzepka
    ArXiv 2018
  • Rafal Rzepka
    Artificial General Intelligence. International Conference, AGI. Proceedings: Lecture Notes in Artificial Intelligence (LNAI 10999) 2018
  • Ikl{\'e}, M., Franz, A., Rzepka, R., Goertzel, B.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10999 LNAI 1611-3349 2018
  • Michal Ptaszynski, Fumito Masui, Yoko Nakajima, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (7) 1189 - 1201 1883-8014 2017/11/01 [Refereed][Not invited]
     
    This paper presents a novel method of analyzing morphosemantic patterns in language to the detect cyberbullying, or frequently appearing harmful messages and entries that aim to humiliate other users. The morphosemantic patterns represent a novel concept, with the assumption that analyzed elements can be perceived as a combination of morphological information, such as parts of speech, and semantic information, such as semantic roles, categories, etc. The patterns are further automatically extracted from the data containing harmful entries (cyberbullying) and non-harmful entries found on the informal websites of Japanese high schools. These website data were prepared and standardized by the Human Rights Center in Mie Prefecture, Japan. The patterns extracted in this way are further applied to a document classification task using the provided data in 10-fold crossvalidation. The results indicate that morphosemantic sentence representation can be considered useful in the task of detecting the deceptive and provocative language used in cyberbullying.
  • Be More Eloquent, Professor ELIZA – Comparison of Utterance Generation Methods for Artificial Second Language Tutor
    Taku Nakamura, Rafal Rzepka, Kenji Araki, Kentaro Inui
    Proceedings of Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2017) 2017/08 [Refereed][Not invited]
  • Asystent - a Prototype of a Motivating Electronic Assistant
    Patrycja Swieczkowska, Jolanta Bachan, Rafal Rzepka, Kenji Araki
    Proceedings of Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2017) 2017/08 [Refereed][Not invited]
  • Conscious vs. Unaware Evaluation - Using Collective Intelligence for an Automatic Evaluation of Acts
    Rafal Rzepka, Kenji Araki
    2nd international workshop on evaluating general-purpose AI (EGPAI2017) 2017/08 [Refereed][Not invited]
  • Prototyping A Virtual Reality Game for Commonsense Knowledge Acquisition and Story Understanding
    Rafal Rzepka, Joelle Vitzikam, Nicolas Flasque, Kenji Araki
    Proceedings of Language Sense on Computers IJCAI 2016 Workshop 35 - 40 2017/08 [Refereed][Not invited]
  • Natural Language Processing for Predicting Everyday Behavior with and without Time and Duration Information
    Rafal Rzepka, Kenji Araki
    International Symposium on Forecasting 2017 2017/06 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki, Fumito Masui
    Journal of Open Research Software 1 (16) 16  2049-9647 2017/05 [Refereed][Not invited]
     
    We present ML-Ask – the first Open Source Affect Analysis system for textual input in Japanese. ML-Ask analyses the contents of an input (e.g., a sentence) and annotates it with information regarding the contained general emotive expressions, specific emotional words, valence-activation dimensions of overall expressed affect, and particular emotion types expressed with their respective expressions. ML-Ask also incorporates the Contextual Valence Shifters model for handling negation in sentences to deal with grammatically expressible shifts in the conveyed valence. The system, designed to work mainly under Linux and MacOS, can be used for research on, or applying the techniques of Affect Analysis within the framework Japanese language. It can also be used as an experimental baseline for specific research in Affect Analysis, and as a practical tool for written contents annotation.
  • Yuki Urabe, Rafal Rzepka, Michal Ptaszynski
    JSAI Magazine 32 (3) 356 - 363 2017/05 [Not refereed][Invited]
  • Rafal Rzepka, Noriyuki Okumura, Michal Ptaszynski
    JSAI Magazine 32 (3) 350 - 355 2017/05 [Not refereed][Invited]
  • Noriyuki Okumura, Michal Ptaszynski, Rafal Rzepka
    JSAI Magazine 32 (3) 342 - 349 2017/05 [Not refereed][Invited]
  • Michal Ptaszynski, Noriyuki Okumura, Rafal Rzepka
    JSAI Magazine 32 (3) 333 - 341 2017/05 [Not refereed][Invited]
  • Rafal Rzepka
    The FASEB Journal 2017
  • Michal Ptaszynski, Fumito Masui, Rafal Rzepka, Kenji Araki
    Linguistics and Literature Studies 5 (1) 36 - 50 2017/01 [Refereed][Not invited]
     
    In this paper presents our research in automatic detection of emotionally loaded, or emotive sentences. We define the problem from a linguistic point of view assuming that emotive sentences stand out both lexically and grammatically. To verify this assumption we prepare a text classification experiment. In the experiment we apply language combinatorics approach to automatically extract emotive patterns from training sentences. the applied approach allows automatic extraction of not only widely used unigrams (tokens), or n-grams, but also more sophisticated patterns with disjointed elements. The results of experiments are explained with the use of means such as standard Precision, Recall and balanced F-score. The algorithm also provides a refined list of most frequent sophisticated patterns typical for both emotive and non-emotive context. The method reached results comparable to the state of the art, while the fact that it is fully automatic makes it more efficient and language independent.
  • Rafal Rzepka, Kenji Araki
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10414 178 - 187 1611-3349 2017 [Refereed][Not invited]
     
    It can be said that none of yet proposed methods for achieving artificial ethical reasoning is realistic, i.e. working outside very limited environments and scenarios. Whichever method one chooses, it will not work in various real world situations because it would be very cost-inefficient to provide ethical knowledge for every possible situation. We believe that an autonomous moral agent should utilize existing resources to make a decision or leave it to humans. Inverse reinforcement learning has gathered interest as a possible solution to acquiring knowledge of human values. However, there are two basic difficulties with using a human expert as the source of exemplary behavior. First derives from the fact that it is rather questionable if one person or a few people (even qualified ethicists) can be trusted as safe role models. We propose an approach which requires referring the maximal number of (currently avail-able) possible similar situations to be analyzed, and a majority decision-based “common sense” model is used. The second problem lies in human beings’ difficulties with living up to their words, surrendering to primal urges and cognitive biases, and in consequence, breaking moral rules. Our proposed solution is to use not behaviors but humans’ declared reactions to acts of others in order to help a machine determine what is positive and what is negative feedback. In this paper we discuss how the third person’s opinion could be utilized via means of machine reading and affect recognition to model a safe moral agent and discuss how universal values might be discovered. We also present a simple web-mining system that achieved 85% agreement in moral judgement with human subjects.
  • Pawel Dybala, Motoki Yatsu, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Advances in Intelligent Systems and Computing 483 657 - 669 2194-5357 2017 [Refereed][Not invited]
     
    In this paper, we present our progress so far in realization of project aimed to create a complex, modular humor-equipped conversational system. By complex, we mean that it should be able to: (1) detect users’ emotions, (2) detect users’ humorous behaviors and react to them properly, (3) generate humor according to users’ emotive states and (4) learn each user’s individual sense of humor. The research is conducted in Japanese. We chose puns as a relatively computable genre of humor. We describe a general outline of our system, as well as its four modules: humor detection module, emotion recognition module, response generator module and individualisation module. We present the algorithm of systems used in each module, along with some evaluation results.
  • Automatic Evaluation of Commonsense Knowledge for Refining Japanese ConceptNet
    Seiya Shudo, Rafal Rzepka, Kenji Araki
    Proceeding of 12th Workshop on Asian Language Resources (ALR 2016) 105 - 112 2016/12 [Refereed][Not invited]
  • Exploiting Wikipedia-based Information-rich Taxonomy for Extracting Location and Creator Related Information for ConceptNet Expansion
    Marek Krawczyk, Rafal Rzepka, Kenji Araki
    Proceedings of the 7th Language and Technology Conference - LTC 2015 383 - 387 2016/11 [Refereed][Not invited]
  • Prototyping Radiobots - Automatic Radio Talks Generator Considering Live Feedback from Listeners
    Rafal Rzepka, Yasutomo Kimura, Yoshihito Tsuji, Keiichi Takamaru
    Proceedings of Workshop on Chatbots and Conversational Agents Collocated with IVA2016 2016/09 [Refereed][Not invited]
  • Marek Krawczyk, Rafal Rzepka, Kenji Araki
    KNOWLEDGE-BASED SYSTEMS 108 (7) 125 - 131 0950-7051 2016/09 [Refereed][Not invited]
     
    Our research goal is to generate new assertions suitable for introduction to the Japanese part of the ConceptNet common sense knowledge ontology. In this paper we present a method for extracting IsA assertions (hyponymy relations), AtLocation assertions (informing of the location of an object or place), LocatedNear assertions (informing of neighboring locations) and CreatedBy assertions (informing of the creator of an object) automatically from Japanese Wikipedia XML dump files. We use the Hyponymy extraction tool v1.0, which analyzes definition, category and hierarchy structures of Wikipedia articles to extract IsA assertions and produce an information-rich taxonomy. From this taxonomy we extract additional information, in this case AtLocation, LocatedNear and CreatedBy types of assertions, using our original method. The presented experiments prove that we achieved our research goal on a large scale: both methods produce satisfactory results, and we were able to acquire 5,866,680 IsA assertions with 96.0% reliability, 131,760 AtLocation assertion pairs with 93.5% reliability, 6217 LocatedNear assertion pairs with 98.5% reliability and 270,230 CreatedBy assertion pairs with 78.5% reliability. Our method surpassed the baseline system in terms of both precision and the number of acquired assertions. (C) 2016 Elsevier B.V. All rights reserved.
  • Magnus Ahltorp, Maria Skeppstedt, Shiho Kitajima, Aron Henriksson, Rafal Rzepka, Kenji Araki
    JOURNAL OF BIOMEDICAL SEMANTICS 7 (58) 2041-1480 2016/09 [Refereed][Not invited]
     
    Background: Research on medical vocabulary expansion from large corpora has primarily been conducted using text written in English or similar languages, due to a limited availability of large biomedical corpora in most languages. Medical vocabularies are, however, essential also for text mining from corpora written in other languages than English and belonging to a variety of medical genres. The aim of this study was therefore to evaluate medical vocabulary expansion using a corpus very different from those previously used, in terms of grammar and orthographics, as well as in terms of text genre. This was carried out by applying a method based on distributional semantics to the task of extracting medical vocabulary terms from a large corpus of Japanese patient blogs. Methods: Distributional properties of terms were modelled with random indexing, followed by agglomerative hierarchical clustering of 3x100 seed terms from existing vocabularies, belonging to three semantic categories: Medical Finding, Pharmaceutical Drug and Body Part. By automatically extracting unknown terms close to the centroids of the created clusters, candidates for new terms to include in the vocabulary were suggested. The method was evaluated for its ability to retrieve the remaining n terms in existing medical vocabularies. Results: Removing case particles and using a context window size of 1 + 1 was a successful strategy for Medical Finding and Pharmaceutical Drug, while retaining case particles and using a window size of 8 + 8 was better for Body Part. For a 10n long candidate list, the use of different cluster sizes affected the result for Pharmaceutical Drug, while the effect was only marginal for the other two categories. For a list of top n candidates for Body Part, however, clusters with a size of up to two terms were slightly more useful than larger clusters. For Pharmaceutical Drug, the best settings resulted in a recall of 25 % for a candidate list of top n terms and a recall of 68 % for top 10n. For a candidate list of top 10n candidates, the second best results were obtained for Medical Finding: a recall of 58 %, compared to 46 % for Body Part. Only taking the top n candidates into account, however, resulted in a recall of 23 % for Body Part, compared to 16 % for Medical Finding. Conclusions: Different settings for corpus pre-processing, window sizes and cluster sizes were suitable for different semantic categories and for different lengths of candidate lists, showing the need to adapt parameters, not only to the language and text genre used, but also to the semantic category for which the vocabulary is to be expanded. The results show, however, that the investigated choices for pre-processing and parameter settings were successful, and that a Japanese blog corpus, which in many ways differs from those used in previous studies, can be a useful resource for medical vocabulary expansion.
  • Detecting Cyberbullying with Morphosemantic Patterns
    Michal Ptaszynski, Fumito Masui, Yoko Nakajima, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Joint 8th International Conference on Soft Computing and Intelligent Systems and 17th International Symposium on Advanced Intelligent Systems (SCIS-ISIS 2016) 248 - 255 2016/08 [Refereed][Not invited]
  • Emotion Prediction System for Japanese Language Considering Compound Sentences, Double Negatives and Adverbs
    Rafal Rzepka, Mitsuru Takizawa, Kenji Araki
    Proceedings of Language Sense on Computers IJCAI 2016 Workshop 73 - 79 2016/07 [Refereed][Not invited]
  • Radoslaw Komuda, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of Language Sense on Computers IJCAI 2016 Workshop 67 - 72 2016/07 [Refereed][Not invited]
  • Praiseworthy Acts Recognition Using Web-based Knowledge and Semantic Categories
    Rafal Rzepka, Kohei Matsumoto, Kenji Araki
    Proceedings of the 4th Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2016) co-located with 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), CEUR 1619 41 - 47 2016/07 [Refereed][Not invited]
  • Avoiding Green and Colorless Ideas - Text-based Color-Related Knowledge Acquisition for Better Image Understanding
    Rafal Rzepka, Keita Mitsuhashi, Kenji Araki
    Proceedings of 4th International Workshop on Artificial Intelligence and Cognition AIC 2016 (CEUR vol. 1895) 2016/07 [Refereed][Not invited]
  • Michal Mazur, Krzysztof Karolczak, Rafal Rzepka, Kenji Araki
    International Journal of Distance Education Technologies 14 (3) 52 - 75 1539-3100 2016/07 [Refereed][Not invited]
     
    Vocabulary plays an important part in second language learning and there are many existing techniques to facilitate word acquisition. One of these methods is code-switching, or mixing the vocabulary of two languages in one sentence. In this paper the authors propose an experimental system for computer-assisted English vocabulary learning in context using a code-switching based approach for Japanese learners. First they introduce the CO-MIX system, an English vocabulary teaching system that uses code-switching for vocabulary acquisition. Next, they show how they utilize incidental learning techniques with graded readers to increase language proficiency. The authors present the system architecture, underlying technologies, and evaluate the system's performance through user interaction with both a baseline and the proposed system by using a semantic differential scale. They also perform separate factor analysis of participants' attitudes for both systems, an analysis of users' mistakes and compare users' language tests scores. Finally, the authors discuss the evaluation results and further development of this technology.
  • Michal Ptaszynski, Fumito Masui, Taisei Nitta, Suzuha Hatakeyama, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    International Journal of Child-Computer Interaction 8 15 - 30 2212-8689 2016/05/01 [Refereed][Not invited]
     
    We developed a supporting solution for “cyberbullying” prevention based on recent discoveries in Artificial Intelligence and Natural Language Processing. Cyberbullying, defined as using the Internet to humiliate and slander other people has become a serious problem. In Japan members of the Parent–Teacher Association manually perform Web monitoring to stop cyberbullying activities. Unfortunately, reading through the whole Web manually is an impossible task. Although the complexity of cyberbullying makes it a problem unsolvable solely with the help of technology, we found that technology could make cyberbullying prevention more efficient. We developed a novel method of automatic detection of cyberbullying entries on the Internet. In the method we use seed words from three categories to calculate a semantic orientation score and then maximize the relevance of categories. The proposed method outperformed baseline settings in both laboratory and real world conditions. The developed system was deployed and tested in practice. After a year of testing we noticed a greater than 30 percent-point-drop in its performance. We hypothesize on the reasons for the drop. To regain the lost performance and retain it in the future we propose additional improvements including automatic acquisition and filtering of seed words. Experimentally selected optimal improvements regained much of the lost performance.
  • Global Brain That Makes You Think Twice
    Rafal Rzepka, Michal Mazur, Austin Clapp, Kenji Araki
    AAAI Spring Symposium on Well-Being Computing: AI Meets Health and Happiness Science 403 - 410 2016/03 [Refereed][Not invited]
  • Rafal Rzepka
    Transactions of Japan Society of Kansei Engineering 2016
  • Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    HUMAN LANGUAGE TECHNOLOGY: CHALLENGES FOR COMPUTER SCIENCE AND LINGUISTICS 9561 277 - 289 0302-9743 2016 [Refereed][Not invited]
     
    In this paper we propose a method of automatic distinction between two types of formally identical expressions in Japanese: similes and " metonymical comparisosn", i. e. literal comparisons that include metonymic relations between elements. Expression like "kujira no you na chiisai me" can be translated into English as "eyes small as whale's", while in Japanese, due to the lack of possessive case, it can be misunderstood as "eyes small as a whale". The reason behind this is the presence of metonymic relation between components of such expressions. In the abovegiven example the word "whale" is a metonymy and represents "whale's eye". This is naturally understandable for humans, although formally difficult to detect by automatic algorithms, as both types of expressions (similes and metonymical comparisons) realize the same template. In this work we present a system able to distinguish between these two types of expressions. The system takes a Japanese expression as input and uses the Internet to check possessive relations between its elements. We propose a method of calculating a score based on co-occurrence of source and target pairs in Google (e. g. "whale's eye"). Evaluation experiment showed that the system distinguishes between similes and metonimical comparisons with the accuracy of 74 %. We discuss the results and give some ideas for the future.
  • Rule-based Approach to Extracting Location, Creator and Membership-related Information from Wikipedia-based Information-rich Taxonomy for ConceptNet Expansion
    Marek Krawczyk, Rafal Rzepka, Kenji Araki, Marek Krawczyk
    Proceedings of Language Sense on Computers IJCAI 2016 Workshop 29 - 35 2016 [Refereed][Not invited]
  • Extracting Patterns of Harmful Expressions for Cyberbullying Detection
    Michal Ptaszynski, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Proceedings of the 7th Language and Technology Conference - LTC 2015 370 - 375 2015/11 [Refereed][Not invited]
  • Applying Code-Switching Method for E-Learning in English and Japanese Vocabulary Acquisition Systems
    Michal Mazur, Rafal Rzepka, Kenji Araki
    Proceedings of the 7th Language and Technology Conference - LTC 2015 487 - 491 2015/11 [Refereed][Not invited]
  • Michal Ptaszynski, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    IJCAI 2015 Workshop on INTELLIGENT PERSONALIZATION (IP'2015) 2015/07 [Refereed][Not invited]
  • Haiku Generator That Reads Blogs and Illustrates Them with Sounds and Images
    Rafal Rzepka, Kenji Araki
    Proceedings of 24th International Joint Conference on Artificial Intelligence (IJCAI 2015) 2496 - 2502 2015/07 [Refereed][Not invited]
  • Automatic Narrative Humor Recognition Method Using Machine Learning and Semantic Similarity Based Punchline Detection
    Rafal Rzepka, Yusuke Amaya, Motoki Yatsu, Kenji Araki
    Proceedings of IJCAI 2015 International Workshop on Chance Discovery, Data Synthesis and Data Market 25 - 31 2015/07 [Refereed][Not invited]
  • Toward Artificial Ethical Learners That Could Also Teach You How to Be a Moral Man
    Rafal Rzepka, Kenji Araki
    Proceedings of IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015) 2015/07 [Refereed][Not invited]
  • Replacing Sensors with Text Occurrences for Commonsense Knowledge Acquisition
    Rafal Rzepka, Marek Krawczyk, Kenji Araki
    Proceedings of IJCAI 2015 Workshop on Cognitive Knowledge Acquisition and Applications (Cognitum 2015) 2015/06 [Refereed][Not invited]
  • Language-Centered World Simulator for Testing Various Approaches to Machine Consciousness
    Rafal Rzepka, Kenji Araki
    Proceedings of the Toward a Science of Consciousness (TSC 2015) Conference 210 - 210 2015/06 [Refereed][Not invited]
  • Conscienceness – the correlation between Machine Ethics and Machine Consciousness
    Radoslaw Komuda, Rafal Rzepka, Kenji Araki
    Proceedings of the Toward a Science of Consciousness (TSC 2015) Conference 208 - 208 2015/06 [Refereed][Not invited]
  • Rafal Rzepka, Kenji Araki
    Rethinking Machine Ethics in the Age of Ubiquitous Technology 73 - 95 2015/05/31 [Refereed][Invited]
     
    This chapter introduces an approach and methods for creating a system that refers to human experiences and thoughts about these experiences in order to ethically evaluate other parties', and in a long run, its own actions. It is shown how applying text mining techniques can enrich machine's knowledge about the real world and how this knowledge could be helpful in the difficult realm of moral relativity. Possibilities of simulating empathy and applying proposed methods to various approaches are introduced together with discussion on the possibility of applying growing knowledge base to artificial agents for particular purposes, from simple housework robots to moral advisors, which could refer to millions of different experiences had by people in various cultures. The experimental results show efficiency improvements when compared to previous research and also discuss the problems with fair evaluation of moral and immoral acts.
  • Rafal Rzepka
    Proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) 2015
  • Rafal Rzepka
    ArXiv 2015
  • Rafal Rzepka
    International Journal of Multimedia Information Retrieval 2015
  • Michal Ptaszynski, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Human Language Technology. Challenges for Computer Science and Linguistics - 7th Language and Technology Conference(LTC) 349 - 362 2015
  • Rafal Rzepka, Kenji Araki
    Intelligent Systems, Control and Automation: Science and Engineering 74 257 - 272 2213-8994 2015 [Refereed][Invited]
     
    Our methods for realizing a moral artificial agent assume that the wisdom of crowds can equip a machine with the enormous number of experiences that are the source of its ethical reasoning. Every second, people with different cultural, religious or social backgrounds share their personal experiences about multitudes of human acts. We propose that a machine therapist capable of analyzing thousands of such cases should be more convincing and effective talking to a patient, instead of analyzing single keywords. In this chapter, we introduce this vision and several techniques already implemented in an algorithm for generating empathic machine reactions based on emotional and social consequences. We show the roles that Bentham’s Felicific Calculus, Kohlberg’s Theory of Stages of Moral Development and McDougall’s classification of instincts play in the agent’s knowledge acquisition, and we describe the accuracy of already working parts. Modules and lexicons of phrases based on these theories enable a medical machine to gather information on how patients typically feel when certain events happen, and what could happen before and after actions. Such empathy is important for understanding the actions of other people, and for learning new skills by imitation. We also discuss why this bottom-up approach should be accompanied by a top-down utility calculation to ensure the best outcome for a particular user, and what ethical dilemmas an advanced artificial therapist could cause.
  • Marek Krawczyk, Rafal Rzepka, Kenji Araki
    2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS 2985 - 2989 1062-922X 2015 [Refereed][Not invited]
     
    This paper presents a method of acquiring IsA assertions (hyponymy relations), AtLocation assertions (informing of location of objects) and LocatedNear assertions (informing of neighboring locations) automatically from Japanese Wikipedia XML dump files. To extract IsA assertions, we use the Hyponymy extraction tool v1.0, which analyses definition, category and hierarchy structures of Wikipedia articles. The tool also produces information-rich taxonomy from which, using our original method, we can extract additional information, in this case AtLocation and LocatedNear type of assertions. Experiments showed that both methods produce positive results: we were able to acquire 5,866,680 IsA assertions with 99.0% reliability, 131,760 AtLocation assertion pairs with 93.0% reliability and 6,217 LocatedNear assertion pairs with 99.0% reliability. Our method exceeded the baseline system considering both precision and the number of acquired assertions.
  • Rafal Rzepka
    STRUCTURAL DIFFERENTIATION IN SOCIAL MEDIA: ADHOCRACY, ENTROPY, AND THE 1 % EFFECT 23 - 39 2015 [Refereed][Not invited]
  • 北嶋志保, ジェプカ・ラファウ, 荒木健治
    知能と情報（日本知能情報ファジィ学会誌） 27 (1) 512 - 526 2015/01 [Refereed][Not invited]
  • Magnus Ahltorp, Hideyuki Tanushi, Shiho Kitajima, Maria Skeppstedt, Rafal Rzepka, Kenji Araki
    Proceedings of the 11th NTCIR Conference 158 - 162 2014/12 [Refereed][Not invited]
  • Yasutomo Kimura, Fumitoshi Ashihara, Arnaud Jordan, Keiichi Takamaru, Yuzu Uchida, Hokuto Ototake, Hideyuki Shibuki, Michal Ptaszynski, Rafal Rzepka, Fumito Masui, Kenji Araki
    Proceedings of the 11th NTCIR Conference 550 - 555 2014/12 [Refereed][Not invited]
  • Medical vocabulary mining using distributional semantics on Japanese patient blogs
    Magnus Ahltorp, Maria Skeppstedt, Shiho Kitajima, Rafal Rzepka, Kenji Araki
    Proceedings of the 6th International Symposium on Semantic Mining in Biomedicine (SMBM 2014) 69 - 73 2014/09 [Refereed][Not invited]
  • Rafal Rzepka
    International Journal of Multimedia Data Engineering and Management (IJMDEM) 5 (2) 1 - 17 2014/09 [Refereed][Invited]
  • Yuki Urabe, Rafal Rzepka, Kenji Araki
    International Journal of Multimedia Data Engineering and Management IGI Global 5 (1) 14 - 33 1947-8534 2014/06 [Refereed][Not invited]
     
    Japanese emoticons are widely used to express users' feelings and intentions in social media, blogs and instant messages. Japanese smartphone keypads have a feature that shows a list of emoticons, enabling users to insert emoticons simply by touching them. However, this list of emoticons contains more than 200, which is difficult to choose from, so a method to reorder the list and recommend appropriate emoticons to users is necessary. This paper proposes an emoticon recommendation method based on the emotive statements of users and their past selections of emoticons. The system is comprised of an affect analysis system and an original emoticon database: a table of 59 emoticons numerically categorized by 10 emotion types. The authors' experiments showed that 73.0% of chosen emoticons were among the top five recommended by the system, which is an improvement of 43.5% over the method used in current smartphones, which is based only on users' past emoticon selections.
  • Rafal Rzepka
    International Journal of Multimedia Data Engineering and Management (IJMDEM) 5 (1) 52 - 64 2014/06 [Refereed][Not invited]
  • Sam S. Adams, Itamar Arel, Joscha Bach, Robert Coop, Rob Furlan, Ben Goertzel, J. Storrs Hall, Alexei Samsonovich, Matthias Scheutz, Matthew Schlesinger, Stuart C. Shapiro, John Sowa, Kosuke Shinoda, Ryutaro Ichise, Rafal Rzepka, Atsushi Terao, Kotaro Funakoshi, Hiroyasu Matsushima, Hiroshi Yamakawa
    Journal of the Japanese Society for Artificial Intelligence 人工知能学会 ; 2014- 29 (3) 241 - 257 2188-2266 2014/05 [Invited]
     
    本論文では人間レベルの汎用人工知能(Artificial General Intelligence:以下AGI)の実現に向けたロードマップの概要を述べる.AGI一般に関する議論から始め,実現への現実的な目標および特性と必要条件に関して基本的な定義を行い,AGIが備えるべき能力の起点となる展望(全体図)を示す.具体的には,発達心理学からAGIの主要テーマを導出し,数学的・生理学的・情報処理的観点から実装に必要な知見を得る.AGIの性能評価に適したタスクと環境を同定し,AGIの全体図上のロードマップを構成するマイルストーンとして七つのシナリオを示すことで,さらなるAGI研究や連携の方向性を提示する.
  • Task-Oriented Consciousness – Will My Robot Feel More Happiness and Freedom Than Me?
    Rafal Rzepka, Kenji Araki
    Anniversary “Toward a Science of Consciousness” Conference,(TSC2014) 2014/04 [Refereed][Not invited]
  • Experience of Crowds as a Guarantee for Safe Artificial Self
    Rafal Rzepka, Kenji Araki
    Tech. Reports from AAAI Spring Symposium on Implementing Selves with Safe Motivational Systems & Self-Improvement 40 - 44 2014/03 [Refereed][Not invited]
  • Michal Ptaszynski, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    Computer Speech and Language 28 (1) 38 - 55 0885-2308 2014 [Not refereed][Not invited]
     
    This paper presents our research on automatic annotation of a five-billion-word corpus of Japanese blogs with information on affect and sentiment. We first perform a study in emotion blog corpora to discover that there has been no large scale emotion corpus available for the Japanese language. We choose the largest blog corpus for the language and annotate it with the use of two systems for affect analysis: ML-Ask for word- and sentence-level affect analysis and CAO for detailed analysis of emoticons. The annotated information includes affective features like sentence subjectivity (emotive/non-emotive) or emotion classes (joy, sadness, etc.), useful in affect analysis. The annotations are also generalized on a two-dimensional model of affect to obtain information on sentence valence (positive/negative), useful in sentiment analysis. The annotations are evaluated in several ways. Firstly, on a test set of a thousand sentences extracted randomly and evaluated by over forty respondents. Secondly, the statistics of annotations are compared to other existing emotion blog corpora. Finally, the corpus is applied in several tasks, such as generation of emotion object ontology or retrieval of emotional and moral consequences of actions. © 2013 Elsevier Ltd.
  • Rafal Rzepka
    ArXiv 2014
  • Rafal Rzepka
    Computers in Human Behavior 2014
  • Michal Ptaszynski, Yoshio Momouchi, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, Kenji Araki
    Mining User Generated Content 189 - 221 2014
  • Michal Ptaszynski, Fumito Masui, Rafal Rzepka, Kenji Araki
    Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis(WASSA@ACL) 59 - 65 2014
  • Ptaszynski Michal, Rzepka Rafal, Oyama Satoshi, Kurihara Masahito, Araki Kenji
    IMT Information and Media Technologies Editorial Board 9 (4) 429 - 445 1881-0896 2014 
    In this paper we present a survey on natural language corpora, with particular focus on corpora of large scale and those applicable to sentiment analysis. Natural language corpora are crucial for training various Software Engineering applications, from part-of-speech taggers and dependency parsers to dialog systems or sentiment analysis software. We compare several natural language corpora created for different languages, analyze their distinctive features and the amount of additional annotations provided by the developers of those corpora.
  • Ptaszynski, M., Rzepka, R., Oyama, S., Kurihara, M., Araki, K.
    Computer Software 31 (2) 2.151-2.167 (J-STAGE)  0289-6540 2014 [Refereed][Not invited]
  • Michal Ptaszynski, Fumito Masui, Rafal Rzepka, Kenji Araki
    KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 35 484 - 493 1877-0509 2014 [Refereed][Not invited]
     
    This paper presents our research in detection of emotive (emotionally loaded) sentences. The task is defined as a text classification problem with an assumption that emotive sentences stand out both lexically and grammatically. The assumption is verified experimentally. The experiment is based on n-grams as well as more sophisticated patterns with disjointed elements. To deal with the sophisticated patterns a novel language modelling algorithm based on the idea of language combinatorics is applied. The results of experiments are explained with the standard means of Precision, Recall and balanced F-score. The algorithm also provides a refined list of most frequent sophisticated patterns typical for both emotive and non-emotive context. (C) 2014 The Authors. Published by Elsevier B.V.
  • Detecting false metaphors in Japanese
    Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    Proceedings of the 6th Language & Technology Conference (LTC’13) 127 - 131 2013/12 [Refereed][Not invited]
  • Fumiko Kano Gluckstad, Tue Helau, Mikkel N. Schmid, Morten Motup, Rafal Rzepka, Kenji Araki
    Proceedings of 2013 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2013) 349 - 354 2013/12 [Refereed][Not invited]
  • Taisei Nitta, Fumito Masui, Michal Ptaszynski, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    Proceedings of the 6th International Joint Conference on Natural Language Processing (IJCNLP 2013) 579 - 586 2013/10 [Refereed][Not invited]
  • Extraction of Drug Information Using Clue Words from Japanese Blogs
    Shiho Kitajima, Rafal Rzepka, Kenji Araki
    Proceedings of Conference of the Pacific Association for Computational Linguistics 2013 (PACLING 2013) 2013/09 [Refereed][Not invited]
  • Applying the Stop List and Part of Speech Analysis to Processing the IEPG Search Query
    Denis Kiselev, Rafal Rzepka, Kenji Araki
    Proceedings of Conference of the Pacific Association for Computational Linguistics 2013 (PACLING 2013) 2013/09 [Refereed][Not invited]
  • Semantic Clues for Novel Metaphor Generator
    Rafal Rzepka, Pawel Dybala, Koichi Sayama, Kenji Araki
    Proceedings of 2nd International Workshop of Computational Creativity, Concept Invention, and General Intelligence 2013/08 [Refereed][Not invited]
  • Michal Ptaszynski, Fumito Masui, Pawel Dybala, Rafal Rzepka, Kenji Araki
    Proceedings of the 1st International Conference on Human-Agent InteractionI (iHAI 2013) 2013/08 [Refereed][Not invited]
  • Cyberbullying Detection Based on Category Relevance Maximization
    Taisei Nitta, Fumito Masui, Michal Ptaszynski, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    20th International Conference on Language Processing and Intelligent Information Systems 2013/06 [Refereed][Not invited]
  • Michal Ptaszynski, Taisei Nitta, Fumito Masui, Yasutomo Kimura, Rafal Rzepka, Kenji Araki
    A open lecture presented on the Faculty of Mathematics and Computer Science of Adam Mickiewicz University 2013/06 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Michal Mazur, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    International Journal of Distance Education Technologies 11 (2) 16 - 47 1539-3100 2013/04 [Refereed][Not invited]
     
    This paper presents research in Contextual Affect Analysis (CAA) for the need of future application in intelligent agents, such as conversational agents or artificial tutors. The authors propose a new term, Computational Fronesis (CF), to embrace the tasks included in CAA applied to development of conversational agents such as artificial tutors. In tutor-student discourse it is crucial that the artificial tutor was able not only to detect user/student emotions, but also to verify toward whom they were directed and whether they were appropriate for the context of the conversation. Therefore, as the first task in CF the authors focus on verification of contextual appropriateness of emotions. They performed some of the first experiments in this task for the Japanese language and discuss future directions in development and implications of Computational Fronesis.Copyright © 2013, IGI Global.
  • Rafal Rzepka, Koichi Muramoto, Kenji Araki
    ALGORITHMIC PROBABILITY AND FRIENDS: BAYESIAN PREDICTION AND ARTIFICIAL INTELLIGENCE 7070 318 - 326 0302-9743 2013 [Refereed][Not invited]
     
    In this paper we introduce our ideas on how experiences from real situations could be processed to decrease what Solomonoff called "Conceptual Jump Size". We introduce applications based on common-sense knowledge showing that vast corpora are able to automatically confirm the validity of the output, and also replace a "trainer", which could lead to decreasing human influence and speeding up the process of finding solutions not provided by such a "trainer" or by real world descriptions. Following this idea, we also suggest a shift toward combining natural languages with programming languages to smoothen transitions between layers of Solomonoff's "Concept net" leading from primitive concepts to a problem solution.
  • Shiho Kitajima, Rafal Rzepka, Kenji Araki
    2013 IEEE SEVENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2013) 383 - 386 2325-6516 2013 [Refereed][Not invited]
     
    Information disclosed to the public by patients is very important for people who are suffering from same illness because such information can be a source of knowledge and encouragement. Our aim is to make a system that extracts, organizes and visually represents information from patients' blogs. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions - drug name, object of change, and its effect - from illness survival blogs. However, conventional extraction methods are not suitable since these blogs are written in free natural language. Therefore, this paper proposes a method to extract the triplets using specific clue words and parsing the results. An evaluation experiment confirmed that medication usage information can be extracted with high accuracy using our proposed method, in comparison to existing methods. Moreover, recall was improved by combining our proposed method and a baseline system.
  • Denis Kiselev, Rafal Rzepka, Kenji Araki
    Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013 146 - 149 2013 [Refereed][Not invited]
     
    This paper describes a system for searching the Web-based Japanese TV program guide. The system features using morphological parsing and part-of-speech analysis to locate words with nominal and attributive semantic features in the query. Such words are matched mandatorily when searching the TV program guide text, while other words are matched optionally. Moreover, certain words and morphemes are removed from the query as they are considered to have little semantic value. The system checks every query against a stop list of such words and morphemes. Other processing methods, e.g. reversing the search phrase word order and allowing zero or more words between the search target words, are also utilized. The present paper uses TV guide search examples to demonstrate how the proposed method can improve Japanese TV program data search results. The paper also contains a few ideas about ways the method could be used for other languages. © 2013 IEEE.
  • Yuki Urabe, Rafal Rzepka, Kenji Araki
    2013 IEEE SEVENTH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2013) 25 - 31 2325-6516 2013 [Refereed][Not invited]
     
    This paper describes the development of an emoticon recommendation system based on emoticons numerically categorized by emotion. The emoticon recommendation system aims to help users express their feelings in computer-mediated communication by recommending emoticons appropriate to user input. In order to develop this system, the original emoticon database, a table of emoticons with the points expressed from each of 10 distinctive emotions, was developed. An evaluation experiment showed that 71.3% of user-selected emoticons were among the top 10 emoticons recommended by the proposed system. Moreover, we compared the proposed system to the current system used in iPhone by adopting a semantic differential (SD) scale of 1-7. The results showed that the proposed system scored higher than the current system by 1.05 points in ease of choice, 0.55 points in accuracy, and 0.55 points in specificity. We plan to make our proposed method open source, so that any developer can build in their own interfaces and enhance their own input methods using these emoticon recommendation systems.
  • Rafal Rzepka, Kenji Araki
    Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 967 - 970 2013 [Refereed][Not invited]
     
    In this paper we introduce an algorithm for affective reasoning based on Bent ham's Felific Calculus known also as the hedonic calculus. Knowledge recquired for the task is retrived from a blog corpus by means of sentiment analysis on sentences containing an action or state input. This approach allows a machine to gather information on how usually other people feel when something happens, why people did it and what could happen after the act. Such knowledge is important for understanding actions of others, and for acquiring emphatic skills by a machine. In addition to emotion categorization of Nakamura, we introduce two lexicons based on McDougall's instinct classification and Kohlbergian stages of moral development, then show some basic efficiency of the retrieved knowledge. © 2013 IEEE.
  • Yuki Urabe, Rafal Rzepka, Kenji Araki
    Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013 1460 - 1461 2013 [Refereed][Not invited]
     
    The existence of social media has made computermediated communication more widespread among users around the world. This paper describes the development of an emoticon recommendation system that allows users to express their feelings with their input. In order to develop this system, an innovative emoticon database consisting of a table of emoticons with points expressed from each of 10 distinctive emotions was constructed. An evaluation experiment showed that 71.3% of user-selected emoticons were among the top 10 emoticons recommended by the proposed system. Copyright 2013 ACM.
  • Rafal Rzepka, Kenji Araki
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8238 370 - 376 0302-9743 2013 [Refereed][Not invited]
     
    In this paper we introduce a method for generating a set of possible reasons of an action needed by an AI program for reasoning about human behavior. We achieve this goal by using web-mining and lexicons of keywords reflecting 14 instincts categories developed by psychologist William McDougall. We describe our system, the experiment and analyze its results of 78% of correct retrievals. The paper is also meant to be a message to social scientists who might be interested in testing their theories on constantly growing group of Internet users. © 2013 Springer International Publishing.
  • Michal Ptaszynski, Hiroaki Dokoshi, Satoshi Oyama, Rafal Rzepka, Masahito Kurihara, Kenji Araki, Yoshio Momouchi
    EXPERT SYSTEMS WITH APPLICATIONS 40 (1) 168 - 176 0957-4174 2013/01 [Refereed][Not invited]
     
    This paper presents our research in text-based affect analysis (AA) of narratives. AA represents a task of estimating or recognizing emotions elicited by a certain semiotic modality. In text-based AA the modality in focus is the textual representation of language. In this research we study particularly one type of language realization, namely narratives (e.g., stories, fairy tales, etc.). Affect analysis within the context of narratives is a challenging task because narratives are created of different kinds of sentences (descriptions, dialogs, etc.). Moreover, different characters become subjects of different emotional expressions in different parts of narratives. In this research we address the problem of person/character related affect recognition in narratives. We propose a method for emotion subject extraction from a sentence based on analysis of anaphoric expressions and compare two methods for affect analysis. We evaluate the system and discuss its possible future improvements. (C) 2012 Elsevier Ltd. All rights reserved.
  • Rafal Rzepka
    ArXiv 2013
  • Rafal Rzepka
    International Journal of Approximate Reasoning 2013
  • Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    2012 INTERNATIONAL CONFERENCE ON ASIAN LANGUAGE PROCESSING (IALP 2012) 33 - 36 2012 [Refereed][Not invited]
     
    In this paper we describe a phonetic classification of Japanese puns (dajare). Basing on real life examples gathered from available sources (books, Internet), we divided Japanese puns into 12 groups with numerous subgroups, according to phonetic changes that occur within them. This classification was prepared for the NLP purpose, i.e. to be used in humor processing. Its usefulness was shown in a research project, aimed at constructing a humor-equipped conversational system for Japanese.
  • Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    ACM International Conference Proceeding Series 2587 - 2590 2012 [Refereed][Not invited]
     
    In this paper we propose a method of filtering excessive amount of textual data acquired from the Internet. In our research on pun generation in Japanese we experienced problems with extensively long data processing time, caused by the amount of phonetic candidates generated (i.e. phrases that can be used to generate actual puns) by our system. Simple, naive approach in which we take into considerations only phrases with the highest occurrence in the Internet, can effect in deletion of those candidates that are actually usable. Thus, we propose a data filtering method in which we compare two Internet-based rankings: a co-occurrence ranking and a hit rate ranking, and select only candidates which occupy the same or similar positions in these rankings. In this work we analyze the effects of such data reduction, considering 1 cases: when the candidates are on exactly the same positions in both rankings, and when their positions differ by 1, 2, 3 and 4. The analysis is conducted on data acquired by comparing pun candidates generated by the system (and filtered with our method) with phrases that were actually used in puns created by humans. The results show that the proposed method can be used to filter excessive amounts of textual data acquired from the Internet. © 2012 ACM.
  • Pawel Dybala, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    Artificial Intelligence of Humor, Papers from the 2012 AAAI Fall Symposium, Arlington, Virginia, USA, November 2-4, 2012 AAAI 2012 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2012
  • Michal Ptaszynski, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    Speech, Image, and Language Processing for Human Computer Interaction: Multi-Modal Advancements 234 - 260 2012 [Refereed][Invited]
     
    Emoticons are string of symbols representing body language in text-based communication. For a long time they have been considered as unnatural language entities. This chapter argues that, in over 40-year-long history of text-based communication, emoticons have gained a status of an indispensable means of support for text-based messages. This makes them fully a part of Natural Language Processing. The fact the emoticons have been considered as unnatural language expressions has two causes. Firstly, emoticons represent body language, which by definition is nonverbal. Secondly, there has been a lack of sufficient methods for the analysis of emoticons. Emoticons represent a multimodal (bimodal in particular) type of information. Although they are embedded in lexical form, they convey non-linguistic information. To prove this argument the authors propose that the analysis of emoticons was based on a theory designed for the analysis of body language. In particular, the authors apply the theory of kinesics to develop a state of the art system for extraction and analysis of kaomoji, Japanese emoticons. The system performance is verified in comparison with other emoticon analysis systems. Experiments showed that the presented approach provides nearly ideal results in different aspects of emoticon analysis, thus proving that emoticons possess features of multimodal expressions. © 2012, IGI Global.
  • Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    AISB/IACAP World Congress 2012: Linguistic and Cognitive Approaches to Dialogue Agents, Part of Alan Turing Year 2012 40 - 49 2012 
    This paper presents YACIS, a new fully annotated large scale corpus of Japanese language. The corpus is based on blog entries from Ameba blog service. The original structure (blog post and comments) is preserved, thanks to which semantic relations between posts and comments are maintained. The corpus is annotated with syntactic (POS, dependency parsing, etc.) and affective (emotive expressions, emoticons, valence, etc.) information. The annotations are evaluated in a survey on over forty respondents. The corpus is also compared to other existing corpora, both large scale and emotion related.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki, Kohichi Sayama
    AISB/IACAP World Congress 2012: Linguistic and Cognitive Approaches to Dialogue Agents, Part of Alan Turing Year 2012 50 - 54 2012 
    In our previous work we proposed an idea of a system able to generate humorous metaphor misunderstanding during conversations with users, employing the mechanism of salience imbalance. However, according to existing research in the field of cognitive science, lexical salience imbalance might not be enough to constitute humorous metaphors. Another important factor in this process can be emotive salience imbalance, i.e. emotional shifts, which occur within metaphorical expressions. In this paper we propose how to employ this mechanism in our system, by implementing an emotion from text detector.
  • Michal Ptaszynski, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis(WASSA@ACL) 89 - 98 2012
  • Language of Emotions for Simulating Moral Imagination
    Rafal Rzepka, Kenji Araki
    Proceedings of the 6th Confetence of Language, Discourse, and Cognition (CLDC 2012) 67 - 67 2012 [Refereed][Not invited]
  • Motoyasu Fujita, Rafal Rzepka, Kenji Araki
    Proceedings of the IASTED International Conference on Artificial Intelligence and Soft Computing, ASC 2011 294 - 299 2011 [Refereed][Not invited]
     
    In this paper, we describe the effectiveness of utterance generation using causal knowledge for a dialogue system. Recently, there has been a variety of research on non-task-oriented dialogue systems however, an effective approach has not yet been developed. One of the most important reasons for this is that non-task-oriented dialogue systems lack common sense knowledge, which is not in their databases. As the first step towards solving this problem, we concentrated on causal knowledge containing reasons and effects, which can provide unwritten meanings for utterance understanding and generating modules. In this paper we investigated how an utterance generated with knowledge related to user input can improve an existing conversational system. Experiment results show that utterance generation using causal knowledge can improve a conversational system.
  • A Modules-Based, Task-Navigational Dialogue System
    Motoki Yatsu, Rafal Rzepka, Kenji Araki
    Proceedings of PACLING 2011 46  2011 [Refereed][Not invited]
  • Introducing Grammatically Aware Regular Expression
    Tyson Roberts, Rafal Rzepka, Kenji Araki
    Proceedings of PACLING 2011 48  2011 [Refereed][Not invited]
  • Svetoslav Dankov, Rafal Rzepka, Kenji Araki
    Procedia - Social and Behavioral Sciences 27 274 - 280 1877-0428 2011 [Refereed][Not invited]
     
    Augmented Reality (AR) applications have become widespread with the continued miniaturization of technology. With the increasing use of smart phones, which often provide increased processing power, enhanced and open software platforms, Augmented Reality has become instrumental in the way we perceive our surroundings and the information that it carries. Augmented Reality has also become a welcome visualization tool for many fields, not restricted to Human-Computer Interaction. In this paper we present a novel approach for building interactive interfaces using Augmented Reality and we give an example how one can use our framework for creating games to collect common sense knowledge from users. We present a software framework for ubiquitous Augmented Reality enhancement for human-computer interaction called UIAR (User Interface through AR). Our framework improves on four areas in Augmented Reality development that we currently see lacking. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of PACLING Organizing Committee.
  • Proposal for a Conversational English Tutoring System that Encourages User Engagement
    Michal Mazur, Rafal Rzepka, Kenji Araki
    Proceedings of the 19th International Conference on Computers in Education : Asia-Pacific Society for Computers in Education (ICCE2011) 10 - 12 2011 [Refereed][Not invited]
  • Rafal Rzepka, Koichi Muramoto, Kenji Araki
    AI 2011: ADVANCES IN ARTIFICIAL INTELLIGENCE 7106 648 - 657 0302-9743 2011 [Refereed][Not invited]
     
    In this paper we introduce three methods for automatic generality evaluation of commonsense statements candidates generated for Open Mind Common Sense (OMCS), which is the basis of Concept Net, a commonsense knowledge base. By using sister terms from Japanese Word Net, our system generates new statements which are automatically evaluated by using WWW co-occurrences and hit number retrieved by a Web search engine. These values are used in three generality judgment methods we propose. Evaluation experiments show that the best of them was "exact match ratio" which achieved accuracy of 62.6% when evaluating general sentences and "co-occurrences in snippets" method scored highest with 48.6% when judging unnatural phrases. Compared to the data without noise elimination, the "exact match ratio" achieved 38.2 points increase in accuracy.
  • Keisuke Takagi, Rafal Rzepka, Kenji Araki
    Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Papers from the 2011 AAAI Spring Symposium, Technical Report SS-11-05, Stanford, California, USA, March 21-23, 2011 AAAI 60 - 65 2011 [Refereed][Not invited]
  • Rafal Rzepka
    AI & SOCIETY 2011
  • Rafal Rzepka
    Infectious Disease Clinics of North America 2011
  • Rafal Rzepka
    ArXiv 2011
  • Michal Ptaszynski, Rafal Rzepka, Yoshio Momouchi
    International Journal of Computational Linguistics Computer Science Journals 2 (1) 24 - 36 2180-1266 2011 [Refereed][Not invited]
     
    A "sentence pattern" in modern Natural Language Processing is often considered as asubsequent string of words (n-grams). However, in many branches of linguistics, like Pragmaticsor Corpus Linguistics, it has been noticed that simple n-gram patterns are not sufficient to revealthe whole sophistication of grammar patterns. We present a language independent architecturefor extracting from sentences more sophisticated patterns than n-grams. In this architecture a"sentence pattern" is considered as n-element ordered combination of sentence elements.Experiments showed that the method extracts significantly more frequent patterns than the usualn-gram approach.
  • MASUI Fumito, RZEPKA Rafal, KIMURA Yasutomo, FUKUMOTO Jun-ichi, ARAKI Kenji
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 日本知能情報ファジィ学会 = Japan Society for Fuzzy Theory and Intelligent Informatics 22 (6) 707 - 719 1347-7986 2010/12/15 [Refereed][Not invited]
     
    In this paper, we propose a method for describing a Japanese word, not with explaining or defining sentences, but with figurative descriptions. Utilizing a simile pattern, our method gathers a large number of noun-noun relations from the World Wide Web. On the basis of those relations and their statistical information, associative pieces of knowledge called descriptors are estimated. The descriptors, which describe a query word figuratively, are sorted by ranking in order of descriptive ability level with generality and locality. Moreover, combining property of figurative relation and some fixed patterns, the descriptors are classified into concept words, attribute words, and the others. As output, a set of sorted descriptors is shown with several types of output forms. Some experiments using a prototype system "Murasaki" have been conducted. The experimental results show that the fundamental performance of our method is significantly better than the bag-of-words approach. Additionally, the responsiveness for hot keywords on information retrieval web sites shows that the outcome of the evaluation had 60% precision, which exceeds that of a common dictionary. The method also functioned effectively in ranking performance (74% on MRR) and classification performance (63% accuracy). Furthermore, it is possible that the proposed method could be comparable to Wikipedia if steady coverage of the figurative descriptions for a query word could be ensured.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 19 (6) 819 - 856 0218-2130 2010/12 [Refereed][Not invited]
     
    The evaluation of subjective aspects of HCI, such as human-likeness, likeability or users' emotions towards computers is still quite a neglected issue, especially in the field of non-task oriented conversational systems (chatterbots). In this paper we try to bridge this gap by proposing a new methodology of evaluation. The methods presented were tested in our research on humor-equipped chatterbots. We describe them in details, discuss their drawbacks and usability. In one of the presented methods we used an emotiveness analysis system, which itself can be considered an AI tool, as it was used to detect users' emotions towards conversational systems, and to perform their automatic evaluation. We also propose some methods that we have not used yet, which, however, seem applicable in this field, such as brain scanning techniques. Finally, we give some ideas that should be addressed in the future.
  • PTASZYNSKI Michal, DYBALA Pawel, RZEPKA Rafal, ARAKI Kenji
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics Japan Society for Fuzzy Theory and Intelligent Informatics = 日本知能情報ファジィ学会 22 (1) 73 - 89 1347-7986 2010/02/15 [Refereed][Not invited]
     
    This paper presents a method for automatic evaluation of conversational agents. The method consists of several steps. First, an affect analysis system is used to detect users' general emotional engagement in the conversation and classify their specific emotional states. Next, we interpret this data with the use of reasoning based on Affect-as-Information Theory to obtain information about users' general attitudes to the conversational agent and its performance. The affect analysis system was also enhanced with a procedure for analysis of Contextual Valence Shifters to help determine the semantic orientation of emotive expressions. The method is used as a background procedure during users' conversations with two Japanese-speaking conversational agents. To verify the usability of the method, the users' attitudes to the conversational agents determined automatically during the conversations were compared to the results of a questionnaire taken after the conversations. The results provided by the system revealed similar tendencies to the questionnaire. Therefore we can say that the method is applicable as a means of evaluation for Japanese-speaking conversational agents.
  • Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    International Journal of Biometrics 2 (2) 134 - 154 1755-8301 2010/02 [Refereed][Not invited]
     
    This paper presents a novel method for estimating speaker's affective states based on two contextual features: valence shifters and appropriateness. Firstly, a system for affect analysis is used to recognise specific types of emotions. We improve the baseline system with the analysis of Contextual Valence Shifters (CVS), which determine the semantic orientation of emotive expressions. Secondly, a web mining technique is used to verify the appropriateness of the recognised emotions for the particular context. Verification of contextual appropriateness of emotions is the next step towards implementation of Emotional Intelligence Framework in machines. The proposed method is evaluated using two conversational agents. Copyright © 2010 Inderscience Enterprises Ltd.
  • Radoslaw Komuda, Michal Ptaszynski, Yoshio Momouchi, Rafal Rzepka, Kenji Arak
    International Journal of Computational Linguistics Research DLINE 1 (3) 155 - 163 0976-416X 2010 [Refereed][Not invited]
     
    We begin this paper by putting forward the topic of human conscience as a metaphysical experience. We present our ongoing research on moral reasoning categories and make first attempts to verify their usefulness in creating an agent with a dynamic algorithm for moral reasoning. Our approach assumes creating such an agent basing on two factors, the idea of wisdom of web-crowd and emotion-buttressed reasoning. We present a novel approach to the idea of ethics. Instead of the usual non-cognitive one we propose a model with computational structure and discuss applicability of this approach. Finally, we present some of the first results of a preliminary experiment performed to prove our approach.
  • Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba, Fumito Masui, Rafal Rzepka, Kenji Araki, Yoshio Momouchi
    International Journal of Computational Linguistics Research DLINE 1 (3) 135 - 163 0976-416X 2010 [Refereed][Not invited]
     
    One of the burning problems lately in Japan has been cyber-bullying, or slandering and bullying people online. The problem has been especially noticed on unofficial Web sites of Japanese schools. Volunteers consisting of school personnel and PTA (Parent-Teacher Association) members have started Online Patrol to spot malicious contents within Web forums and blogs. In practise, Online Patrol assumes reading through the whole Web contents, which is a task difficult to perform manually. With this paper we introduce a research intended to help PTA members perform Online Patrol more efficiently. We aim to develop a set of tools that can automatically detect malicious entries and report them to PTA members. First, we collected cyber-bullying data from unofficial school Web sites. Then we performed analysis of this data in two ways. Firstly, we analysed the entries with a multifaceted affect analysis system in order to find distinctive features for cyber-bullying and apply them to a machine learning classifier. Secondly, we applied a SVM based machine learning method to train a classifier for detection of cyber-bullying. The system was able to classify cyber-bullying entries with 88.2% of balanced F-score.
  • Rafal Rzepka, Shinsuke Higuchi, Michal Ptaszynski, Pawel Dybala, Kenji Araki
    Transactions of the Japanese Society for Artificial Intelligence 人工知能学会 25 (1) 114 - 121 1346-0714 2010 [Refereed][Not invited]
     
    In this paper we propose a method for generating simple but semantically correct replies to user inputs which are not related to a given task of a task-oriented information kiosk or any other natural language interface placed in a public place. We describe our method for retrieving meaningful associations from the Web and adding modality based on chatlog data. After showing the results of the evaluation experiments, we introduce an implementation of an affect analysis algorithm and pun generator to increase users' satisfaction level.
  • Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki
    Proceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention 32 - 38 2010 [Refereed][Not invited]
     
    Thinking of the ways to improve naturalness and adequacy of utterances in conversational agents, the authors propose a dynamic database management system. The system borrows some features of the forgetting mechanisms in humans. The core of the system, forgetting and recalling algorithms, depend on the frequency of usage of context units and their emotive values derived from evaluative reasoning about both sides of interaction - the agent and the user.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention 53 - 58 2010 [Refereed][Not invited]
     
    Humor and emotions are strongly related to each other. Both these topics are recently gaining more and more attention from HCI researchers. Some of existing research projects employ humor generating engines in conversational systems, in order to improve their interaction with humans. Evaluation of such joking systems is often related to users' emotions, i.e. their feelings towards the systems during and after the interaction. However, in human-human interaction, humor is usually a reaction to particular emotional states, and can be even used intentionally, to change interlocutor's emotions (e.g. sadness into joy). Thus, also in HCI it is important to broaden our view and take into consideration not only emotions that follow the humorous act, but also those that precede it. In this paper we propose to distinguish between what we call a "two-stage" approach, in which emotions are seen only as a reaction to humor, and a "multi-stage" approach, in which we consider emotions and humor as an interchanging chain of events, occurring one after another. The multi-stage approach is illustrated with an actual example - our joking conversational system, which 1) analyses users' emotions during the interaction, 2) uses humor as a reaction to users' particular emotional states, and 3) analyses their reactions to it. We briefly present the results of its evaluation experiments, and finally propose an idea of user-adapting humor sense model, in which the humor-emotion chain can be extended even further.
  • Computer System That Likes Chess
    Pawel Dybala, Rafal Rzepka, Kenji Araki
    Proceedings of the Linguistic And Cognitive Approaches To Dialog Agents Symposium 42 - 44 2010 [Refereed][Not invited]
  • Radoslaw Komuda, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention 17 - 19 2010 [Refereed][Not invited]
     
    In this paper we firstly discuss some of the presumptions about human conscience. By a detailed guidance throughout our ongoing research on morality judgment categories we make an attempt to summarize it and its usefulness for creating an explicit moral reasoning agent with system based on the wisdom of the web-crowd. With special remarks on giving it a mathematically calculative structure and chances created by this approach alteration.
  • Machine learning and affect analysis against cyber-bullying
    Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba, Fumito Masui, Rafal Rzepka, Kenji Araki
    Proceedings of the Linguistic And Cognitive Approaches To Dialog Agents Symposium 7 - 16 2010 [Refereed][Not invited]
  • Automatic Haiku Generation Using Web search and Japanese Weblogs as Input
    Takuya Emori, Rafal Rzepka, Kenji Araki
    Proceedings of the International Workshop on Modern Science and Technology (IWMST 2010) 30 - 32 2010 [Refereed][Not invited]
  • A Multi-Input Approach for a System for Semantically Relevant Art Creation
    Tyson Roberts, Rafal Rzepka, Kenji Araki
    Proceedings of the International Workshop on Modern Science and Technology (IWMST 2010) 38 - 39 2010 [Refereed][Not invited]
  • Acquisition of Japanese Word Descriptions from World Wide Web
    Fumito Masui, Rafal Rzepka, Yasutomo Kimura, Junichi Fukumoto, Kenji Araki
    Proceedings of the International Workshop on Modern Science and Technology (IWMST 2010) 153 - 158 2010 [Refereed][Not invited]
  • Ubiquitous User Interfaces Framework Using Augmented Reality as a Platform
    Svetoslav Dankov, Rafal Rzepka, Kenji Araki
    Proceedings of the International Workshop on Modern Science and Technology (IWMST 2010) 187 - 191 2010 [Refereed][Not invited]
  • Co-Mix Project: Towards Artificial Tutors Using Code Mixing as Foreign Language Teaching Method
    Michal Mazur, Rafal Rzepka, Kenji Araki
    Proceedings of the International Workshop on Modern Science and Technology (IWMST 2010) 196 - 201 2010 [Refereed][Not invited]
  • Social Factors in Kohlberg's Theory of Stages of Moral Development - the Utility of (Web) Crowd Wisdom for Machine Ethics Research
    Radoslaw Komuda, Rafal Rzepka, Kenji Araki
    Proceedings of The 5th International Conference on Applied Ethics 34 - 34 2010 [Refereed][Not invited]
  • Pawel Dybala,Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3 IFAAMAS 1433 - 1434 2010 [Refereed][Not invited]
  • Tyson Michael Roberts, Rafal Rzepka, Kenji Araki
    Commonsense Knowledge, Papers from the 2010 AAAI Fall Symposium, Arlington, Virginia, USA, November 11-13, 2010 AAAI 88 - 89 2010 [Refereed][Not invited]
  • Michal Ptaszynski, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, Kenji Araki
    PROCEEDINGS OF THE TWENTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI-10) 1026 - 1032 2010 [Refereed][Not invited]
     
    This paper presents CAO, a system for affect analysis of emoticons. Emoticons are strings of symbols widely used in text-based online communication to convey emotions. It extracts emoticons from input and determines specific emotions they express. Firstly, by matching the extracted emoticons to a raw emoticon database, containing over ten thousand emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing "mouths" or "eyes", based on the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is firstly based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.
  • Michal Ptaszynski, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, Kenji Araki
    IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 1 (1) 46 - 59 1949-3045 2010/01 [Refereed][Not invited]
     
    This paper presents CAO, a system for affect analysis of emoticons in Japanese online communication. Emoticons are strings of symbols widely used in text-based online communication to convey user emotions. The presented system extracts emoticons from input and determines the specific emotion types they express with a three-step procedure. First, it matches the extracted emoticons to a predetermined raw emoticon database. The database contains over 10,000 emoticon samples extracted from the Web and annotated automatically. The emoticons for which emotion types could not be determined using only this database, are automatically divided into semantic areas representing "mouths" or "eyes," based on the idea of kinemes from the theory of kinesics. The areas are automatically annotated according to their co-occurrence in the database. The annotation is first based on the eye-mouth-eye triplet, and if no such triplet is found, all semantic areas are estimated separately. This provides hints about potential groups of expressed emotions, giving the system coverage exceeding 3 million possibilities. The evaluation, performed on both training and test sets, confirmed the system's capability to sufficiently detect and extract any emoticon, analyze its semantic structure, and estimate the potential emotion types expressed. The system achieved nearly ideal scores, outperforming existing emoticon analysis systems.
  • Pawel Dybala, Michal Ptaszynski, Jacek Maciejewski, Mizuki Takahashi, Rafal Rzepka, Kenji Araki
    JOURNAL OF AMBIENT INTELLIGENCE AND SMART ENVIRONMENTS 2 (1) 31 - 48 1876-1364 2010 [Refereed][Not invited]
     
    In this paper we present an innovative work on a multiagent joking conversational system. In our research so far we have shown that implementing humor into a chatterbot can visibly improve its performance. The results presented in this paper are the outcome of the next step of our work. They show that a multiagent system, combining a conversational agent, a pun generator and an emotiveness analysis engine, works reasonably well in interactions with users. In the setup used in this research, the emotiveness analysis agent analyses users' utterances and decides whether it is appropriate to tell a pun. Depending on the results of this analysis, the agent chooses either the pun generator, if the decision is that a joke should be told, or the non-humor-equipped agent when the decision is different. Two evaluation experiments were conducted: user (first person) focused and automatic (emotiveness-analysis-based). In both, we compared the performance of the multiagent joking system and a baseline (non-humorous) conversation agent. The results show that in both cases the humor-equipped engine was evaluated as better than the baseline agent. The results are discussed and some ideas for the future are given.
  • Rafal Rzepka
    ArXiv 2010
  • Rafal Rzepka
    ArXiv 2010
  • Rafal Rzepka
    ArXiv 2010
  • An Automatic Evaluation Method for Conversational Agents Based on Affect-as-Information Theory
    Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    知能と情報（日本知能情報ファジィ学会誌）特集 感情 2 (2) 134 - 155 2010 [Refereed][Not invited]
  • Towards Socialized Machines: Emotions and Sense of Humour in Conversational Agents
    Michal Ptaszynski, Pawel Dybala, Shinsuke Higuhi, Wenhan Shi, Rafal Rzepka, Kenji Araki
    Web Intelligence and Intelligent Agents 9  2010 [Refereed][Not invited]
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    International Journal of Computational Linguistics Research DLINE 1 (3) 116 - 125 0976-416X 2010 [Refereed][Not invited]
     
    The issues of humor and emotions are strongly related to each other. Their role is recently being appreciated by HCI researchers, and numerous scientific ventures are launched to investigate this subject from various perspectives. Among others, research projects exist focusing on implementing humor generators into conversational systems, in order to facilitate their interaction with humans. In such research, evaluation experiments are often focused, if not limited to, examining users' emotive reactions towards systems (both during and after the interactions). However, in HHI (Human- Human Interaction),humor is often used, be it intentionally or subconsciously, in reaction to interlocutor's emotions, in order to change them from negative to positive, as, for instance, anxiety into joy, or at least reduce the degree of negativity. This was showed in numerous research, mostly in the field of psychology. Thus, it can be said that in HHI, both emotions preceding and following humorous acts are taken into consideration, while most research in HCI focus only on the latter, thus representing what we call a "twostage approach" to humor and emotions. In this paper we first describe current state of the art in the field of research on humor and emotions, propose to distinguish two types of approach to relation between these two issues, next we compare existing research on this matter in HHI (psychology) and HCI, and describe our research progress. In our project, launched to construct a humor-equipped conversational system, we developed a system, which 1) analyses users' emotions during the interaction, 2) uses humor as a reaction to users' particular emotional states, and 3) analyses their reactions to it. Thus, the system represents what we defined as a "multi-stage approach" to humor and emotions, taking into consideration users' emotive states both before and after humorous stimuli. We present the results of experiments conducted to evaluate our system's performance, and propose an idea of a user-adapting humor sense model, in which the humor-emotion chain can be extended even further.
  • Ptaszynski Michal, Rzepka Rafal, Araki Kenji
    Proceedings of the Fuzzy System Symposium 日本知能情報ファジィ学会 26 212 - 212 1882-0212 2010 
    Research on Emotions within Artificial Intelligence and related fields has flourished rapidly through several years. Unfortunately, in much research the contextuality of emotions is disregarded. In this paper we argue, that recognizing emotions without recognizing their context is incomplete and cannot be sufficient for real-world applications. We present logical underpinnings of this claim and describe some consequences of disregarding the context of emotions. We also present our approach, in which this context is considered and describe some of the first experiments performed in this matter. The paper is finalized with a discussion on future development and applications of context processing within Affective Computing.
  • Rzepka Rafal, Higuchi Shinsuke, Ptaszynski Michal, Dybala Pawel, Araki Kenji
    Information and Media Technologies Information and Media Technologies 編集運営会議 5 (1) 216 - 223 1881-0896 2010 [Refereed][Not invited]
     
    In this paper we propose a method for generating simple but semantically correct replies to user inputs which are not related to a given task of a task-oriented information kiosk or any other natural language interface placed in a public place. We describe our method for retrieving meaningful associations from the Web and adding modality based on chatlog data. After showing the results of the evaluation experiments, we introduce an implementation of an affect analysis algorithm and pun generator to increase users' satisfaction level.
  • Rafal Rzepka, Wenhan Shi, Michal Ptaszynski, Pawel Dybala, Shinsuke Higuchi, Kenji Araki
    International Conference on Intelligent User Interfaces, Proceedings IUI 487 - 488 2009 [Refereed][Not invited]
     
    By our1 demonstration we want to introduce our achievements in combining different purpose algorithms to build a chatbot which is able to keep a conversation on any topic. It uses snippets of Internet search results to stay within a context, Nakamura's Emotion Dictionary to detect an emotional load existence and categorization of a textual utterance and a causal consequences retrieval algorithm when emotive features are not found. It is also able to detect a possibility to make a pun by analyzing the input sentence and create one if timing is adequate.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS 5484 452 - 461 0302-9743 2009 [Refereed][Not invited]
     
    This paper investigate, the role of humor in non-task oriented (topic restriction free) human-computer dialogue, as well as the correlation between humor and emotions elicited by it in users. A joke-telling conversational system, constructed for the needs of this research, was evaluated by the users as better and more human-like than a baseline system without humor. Automatic emotive evaluation with the usage of an emotiveness analysis system showed that the system with humor elicited more emotions than the other one, and most of them (almost 80%) were positive. This shows that the presence of humor makes computers easier to familiarize with and simply makes users feel better. Therefore, humor should be taken into consideration in research On user-friendly applications, as it enhances the interaction between user and system. The results are discussed and Our concept of a user-adapted humor-equipped system is presented.
  • Artificial Self Based on Collective Mind - Using Common Sense and Emotions Web-Mining for Ethically Correct Behaviors
    Rafal Rzepka, Kenji Araki
    Proceedings of Toward a Science of Consciousness TCS 2009 - Investigating Inner Experience Brain, Mind and Technology 75  2009 [Refereed][Not invited]
  • Shifting Valence Helps Verify Contextual Appropriateness of Emotions
    Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    Proceedings of Workshop on Automated Reasoning about Context and Ontology Evolution (ARCOE-09) 19 - 21 2009 [Refereed][Not invited]
  • Subjective, But Not Worthless - Non-linguistic Feature of Chatterbot Evaluations
    Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of Workshop on Knowledge and Reasoning in Practical Dialogue Systems (KRPDS 2009) 87 - 92 2009 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Arak
    Proceedings of the International Conference on Computational Intelligence (CI 2009) 1 - 6 2009 [Refereed][Not invited]
     
    This paper presents a method for estimating contextual appropriateness of speaker's emotions. The method is using an affect analysis system to estimate the speaker's emotions and a Web mining technique gathering from the Internet associations about emotional common-sense. The baseline of the Web mining technique, using all of the Web, is improved by restricting the query field to the contents of blogs as containing more evaluative information. A conversational agent equipped with this system could choose an appropriate conversational procedure. The proposed method is evaluated using two conversational agents. The use of blog contents improved the method in the aspect of quality as well as time of processing.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of the International Conference on Computational Intelligence (CI 2009) 7 - 14 2009 [Refereed][Not invited]
     
    Human-likeness of dialogue systems is an important, albeit neglected issue. In this paper, basing on evaluation experiments of humor-equipped chatterbot, we propose a method of measuring the distance between humans and systems and relation between human-likeness and humor. The results show that the presence of humor can enhance the performance of dialogue systems. A humor-equipped chatterbot was evaluated as more human like and generally better than one without humor, by both first and third person evaluators. The implications of this fact and novelty of evaluation method are discussed, and some ideas for the future are given.
  • Teaching a Humanoid Robot through Physical Feedback: So Easy Even a Five Year Old Could Use It
    Dai Hasegawa, Rafal Rzepka, Kenji Araki
    Proceedings of the 19th Intelligent System Symposium (FAN2009) and the 1st International Workshop on Aware Computing (IWAC2009) 26  2009 [Refereed][Not invited]
  • Determining the Output of Dependency Parser by Extending Grammar Rules with Weights
    Jacek Maciejewski, Rafał Rzepka, Kenji Araki
    Proceedings of PACLING 2009 121 - 124 2009 [Refereed][Not invited]
  • Crossing Word Borders - Towards Phrasal Pun Generation Engine
    Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Proceedings of PACLING 2009 242 - 247 2009 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki
    Proceedings of PACLING 2009 223 - 226 2009 [Refereed][Not invited]
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    IEICE Transactions on Information and Systems E92-D (12) 2394 - 2401 1745-1361 2009 [Refereed][Not invited]
     
    The topic of Human Computer Interaction (HCI) has been gathering more and more scientific attention of late. A very important, but often undervalued area in this field is human engagement. That is, a person's commitment to take part in and continue the interaction. In this paper we describe work on a humor-equipped casual conversational system (chatterbot) and investigate the effect of humor on a user's engagement in the conversation. A group of users was made to converse with two systems: one with and one without humor. The chat logs were then analyzed using an emotive analysis system to check user reactions and attitudes towards each system. Results were projected on Russell's two-dimensional emotiveness space to evaluate the positivity/negativity and activation/deactivation of these emotions. This analysis indicated emotions elicited by the humorequipped system were more positively active and less negatively active than by the system without humor. The implications of results and relation between them and user engagement in the conversation are discussed. We also propose a distinction between positive and negative engagement. Copyright © 2009 The Institute of Electronics.
  • Dai Hasegawa, Jonas Sjöbergh, Rafal Rzepka, Kenji Araki
    Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009, October 14-16, 2009, Stanford, California, USA 2009 [Refereed][Not invited]
  • Dai Hasegawa, Rafal Rzepka, Kenji Araki
    Proceedings of the Fifth Artificial Intelligence and Interactive Digital Entertainment Conference, AIIDE 2009, October 14-16, 2009, Stanford, California, USA 2009 [Refereed][Not invited]
  • Rafal Rzepka, Radoslaw Komuda, Kenji Araki
    Biologically Inspired Cognitive Architectures, Papers from the 2009 AAAI Fall Symposium, Arlington, Virginia, USA, November 5-7, 2009 AAAI 123  2009 [Refereed][Not invited]
  • Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    Biologically Inspired Cognitive Architectures, Papers from the 2009 AAAI Fall Symposium, Arlington, Virginia, USA, November 5-7, 2009 AAAI FS-09-01 101 - 102 2009 [Refereed][Not invited]
     
    By this paper we would like to open a discussion on the need of Emotional Intelligence as a feature in machines interacting with humans. However, we restrain from making a statement about the need of emotional experience in machines. We argue that providing machines computable means for processing emotions is a practical need requiring implementation of a set of abilities included in the Emotional Intelligence Framework. We introduce our methods and present the results of some of the first experiments we performed in this matter. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.
  • Hasegawa Dai, Rafal Rzepka, Kenji Araki
    Vienna, Austria : Humanoid Robots I-Tech Education and Publishing 65 - 82 2009 [Refereed][Not invited]
  • PTASZYNSKI Michal, DYBALA Pawel, SHI Wenhan, RZEPKA Rafal, ARAKI Kenji
    Journal of Japan Society for Fuzzy Theory and Intelligent Informatics Japan Society for Fuzzy Theory and Intelligent Informatics 21 (2) 30 - 49 1347-7986 2009 [Refereed][Not invited]
     
    We propose a method for affect analysis of textual input in Japanese supported with Web mining. The method is based on a pragmatic reasoning that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. It means that if an emotive expression is used in a sentence in a context described as emotive, the emotion conveyed in the text is revealed by the used emotive expression. The system ML-Ask (Emotive Elements / Expressions Analysis System) is constructed on the basis of this idea. An evaluation of the system is performed in which two evaluation methods are compared. To choose the most objective evaluation method we compare the most popular method in the field and a method proposed by us. The proposed evaluation method was shown to be more objective and revealed the strong and weak points of the system in detail. In the evaluation experiment ML-Ask reached human level in recognizing the general emotiveness of an utterance (0.83 balanced F-score) and 63% of human level in recognizing the specific types of emotions. We support the system with a Web mining technique to improve the performance of emotional state types extraction. In the Web mining technique emotive associations are extracted from the Web using co-occurrences of emotive expressions with morphemes of causality. The Web mining technique improved the performance of the emotional states types extraction to 85% of human performance.
  • Pawel Dybala, Michal Ptaszynski, Rafal Rzepka, Kenji Araki
    8th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2009) 1171 - 1172 2009 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS 1469 - 1474 2009 [Refereed][Not invited]
     
    This paper presents a novel approach to the estimation of user's affective states in Human-Computer Interaction. Most of the present approaches divide emotions strictly between positive or negative. However, recent discoveries in the field of Emotional Intelligence show that emotions should be rather perceived as context-sensitive engagements with the world. This leads to a need to specify whether the emotions conveyed in a conversation are appropriate for a situation they are expressed in. In the proposed method we use a system for affect analysis on textual input to recognize users emotions and a Web mining technique to verify the contextual appropriateness of those emotions. On this basis a conversational agent can choose to either sympathize with the user or help them manage their emotions. Finally, the results of evaluation of the proposed method with two different conversational agents are discussed, and perspectives for further development of the method are proposed.
  • Dai Hasegawa, Jonas Sjobergh, Rafal Rzepka, Kenji Araki
    Proceedings of the First International Workshop on Laughter in Interaction and Body Movement (LIBM'08) 8 - 13 2008 [Refereed][Not invited]
  • Shinsuke Higuchi, Rafal Rzepka, Kenji Araki
    Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing 382 - 390 2008 [Refereed][Not invited]
  • Rafal Rzepka, Shinsuke Higuchi, Michal Ptaszynski, Kenji Araki
    The Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC'08) 2171 - 2175 1062-922X 2008 [Refereed][Not invited]
     
    This paper introduces an early stage of a smart toy development project which combines several techniques to achieve a level of conversational skills and knowledge higher than currently available robots for children. We describe our ideas and achievements for three modules which we treat as the most important - topic unlimited talking engine, emotions recognizer and the moral behavior analyzer. We will also mention our novel evaluation method for freely speaking agents and possibilities of adding another module - an automatic joke generator.
  • Disentangling emotions from the Web Internet in the service of affect analysis
    Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    Proceedings of The Second International Conference on Kansei Engineering & Affective Systems, 51 - 56 2008 [Refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Shinsuke Higuchi, Rafal Rzepka, Kenji Araki
    2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2 901 - 906 2008 [Refereed][Not invited]
     
    In this paper we propose a novel method for automatic evaluation of conversational agents. The method is based on analyzing the user's affect conveyed in utterances. From analyzing: the user's general emotional engagement in the conversation and the emotion types conveyed by the user in the conversation, a simple psychological reasoning is derived about the user's sentiment about the agent's performance. The evaluation experiment on two Japanese-speaking conversational agents showed the same tendencies in the results returned by the system constructed on the proposed method and the user's opinion about the two agents checked in the afterward survey. Thus the method can be used for evaluation of Japanese-speaking conversational agents.
  • Michal Ptaszynski, Pawel Dybala, Shinsuke Higuchi, Rafal Rzepka, Kenji Araki
    Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008 495 - 500 2008 [Refereed][Not invited]
     
    This paper presents a novel method for automatic evaluation of conversational agents. In the method, information about users' attitudes and sentiments to conversational agents and their performance are achieved by analyzing their general emotional engagement in the conversation and specific affective states, and interpreting them using psychological reasoning of Affect-as-Information. In the evaluation experiment the users' attitudes to two Japanesespeaking conversational agents were checked simultaneously in a survey and using a system constructed on the proposed method. The results returned by the system revealed similar tendencies as the survey. Therefore the method is applicable as a mean of evaluation for Japanese-speaking conversational agents. © 2008 IEEE.
  • How to find love in the Internet? Applying Web mining to affect recognition from textual input
    Michal Ptaszynski, Pawel Dybala, Wenhan Shi, Rafal Rzepka, Kenji Araki
    Proceedings of the 2008 Empirical Methods for Asian Languages Processing Workshop (EMALP'08) at The Tenth Pacific Rim International Conference on Artificial Intelligence (PRICAI'08) 67 - 79 2008 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2008
  • Rafal Rzepka
    ArXiv 2008
  • Rafal Rzepka
    Lecture Notes in Computer Science 2008
  • Pawel Dybala, Michal Ptaszynski, Shinsuke Higuchi, Rafal Rzepka, Kenji Araki
    Springer-Verlag Lecture Notes in Artificial Intelligence (LNAI) 5360 214 - 225 0302-9743 2008 [Refereed][Not invited]
     
    This paper contains the results of evaluation experiments conducted to investigate if implementation of a pun generator into a non-task oriented talking system improves the latter's performance. We constructed a simple joking conversational system and conducted one user evaluation experiment and two third person evaluation experiments. The results showed that humor does have a positive influence on the dialogue between humans and computers. The implications of this fact and problems that occurred during the research arc discussed. We also propose how they can be solved in the future.
  • Toward Automatic Support For Japanese Lay Judge System - Processing Precedent Factors For Sentencing Trends Discovery
    Rafal Rzepka, Masafumi Matsuhara, Yasutomo Kimura, Keiichi Takamaru, Hideyuki Shibuki, Koji Murakami
    Proceedings of NTCIR-7 Workshop Meeting 2008 [Refereed][Not invited]
  • Rafal Rzepka
    Proceedings of the Second International Symposium on Universal Communication 2008 [Refereed][Not invited]
  • How We Did How, What and Why ? HOMIO’s Participation in QAC4 of NTCIR-6
    Yasutomo Kimura, Kenji Ishida, Hirotaka Imaoka, Fumito Masui, Keisuke Kameyama, Rafal Rzepka, Kenji Araki
    Proceedings of NTCIR-6 Workshop Meeting 483 - 486 2007 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2007
  • Rafal Rzepka
    ArXiv 2007
  • Rafal Rzepka
    Lecture Notes in Computer Science 4830 LNAI 664 - 668 1611-3349 2007 [Refereed][Not invited]
  • Rafal Rzepka
    Journal of Advanced Computational Intelligence and Intelligent Informatics 10 (6) 868 - 875 1343-0130 2006/11/20 [Refereed]
     
    This paper is to suggest opportunities for advanced systems hiding in the millions of WWW pages. While usually the Internet is used for achieving knowledge for humans, we present opposite approach where a machine retrieves usual knowledge about humans, their common behaviors and feelings. We claim that in long run such capability will be necessary for every machine interacting with a human user. We will concentrate on our theories and illustrate them with the results of web-mining experiment.
  • What About Tests In Smart Environments? On Possible Problems With Common Sense In Ambient Intelligence
    Rafal Rzepka, Kenji Araki
    Proceedings of 2nd IJCAI Workshop on Artificial Intelligence Techniques for Ambient Intelligence 92 - 96 2006 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2006
  • Commonsense Retrieval as an Aid for Easier Conversation-based Language Acquisition
    Rafal Rzepka, Kenji Araki
    Proceedings of Workshop on Computational Modeling of Lexical Acquisition 2005/07 [Refereed][Not invited]
  • Rafal Rzepka, Yali Ge, Kenji Araki
    Proceedings of IJCAI 2005 - Nineteenth International Joint Conference on Artificial Intelligence paper 490 1696 - 1697 2005 [Refereed][Not invited]
     
    In this research we investigated user's behavior while facing a system coping with common knowledge about keywords and compared it with not only classic word-spotting method but also with random text-mining. We show how even a simple implementation of our idea can enrich the conversation and increase the naturalness of computer's utterances. Our results show that even very commonsensical utterances are more natural than classic approaches and also methods we developed to make a conversation more interesting. For arousing opinion exchange during the session, we will also briefly introduce our idea of combining latest NLP achievements into one holistic system where the main engine we want to base on commonsense processing and affective computing.
  • Yali Ge, Rafal Rzepka, Kenji Araki
    Intelligent Information Processing and Web Mining, Proceedings 51 - 58 1615-3871 2005 [Refereed][Not invited]
     
    This paper introduces our method for automatic Schankian-like scripts retrieval from the Internet resources and its preliminary results which might be interesting for Social Sciences researchers. We describe the first module of our system, which is supposed to automatically retrieve commonsensical knowledge from the Web resources by using web-mining techniques. It retrieves minimal "object - action - action" scripts which show humans' common activities changing due the origin of a webpage author. Such data can be used in fields of economics, psycholinguistics, sociolinguistics, psychology, sociology or in language education. By this paper we would like to make NLP researchers notice the potential of commonsense retrieval and encourage them to consider creating such tools for their languages.
  • What Statistics Could Do for Ethics? - The Idea of Common Sense Processing Based Safety Valve
    Rafal Rzepka, Kenji Araki
    Machine Ethics, Papers from AAAI Fall Symposium, Technical Report FS-05-06 85 - 87 2005 [Refereed][Not invited]
  • Yali Ge, Rafal Rzepka, Kenji Araki
    Proceedings of KES'2005 9th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems,Springer-Verlag LNAI 3682 950 - 956 0302-9743 2005 [Refereed][Not invited]
     
    This paper introduces our method for causal knowledge retrieval from the Internet resources, its results and evaluation of using it in utterance creation process. Our system automatically retrieves common-sensical knowledge from the Web resources by using simple web-mining and information extraction techniques. For retrieving causal knowledge the system uses three of specific several Japanese "if" forms. From the results we can conclude that Japanese web pages indexed by a common search engine spiders are enough to discover common causal relationships and this knowledge can be used for making Human-Computer Interfaces sound more natural and interesting than while using classic methods.
  • Three Systems and One Verifier - HOKUM's Participation in QAC3 of NTCIR -
    Yasutomo Kimura, Kenji Ishida, Hirotaka Imaoka, Fumito Masui, Marcin Skowron, Rafal Rzepka, Kenji Araki
    Proceedings of NTCIR-5 Workshop Meeting 402 - 408 2005 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2005
  • Automatic Scripts Retrieval and Its Possibilities for Soft Science Support Applications
    Yali Ge, Rafal Rzepka, Kenji Araki
    Intelligent Information Systems 2005 - New Trends in Intelligent Information Processing and Web Mining Conference Proceedings 3682 (2005) 950 - 956 2005 [Refereed][Not invited]
  • Rafal Rzepka
    ArXiv 2004
  • Toward Fully Automatic Categorization for Commonsense Processing
    Rafal Rzepka, Toshihiko Itoh, Kenji Araki
    Proceedings of the Language Sense on Computer 2004 - Part of the Eight Pacific Rim International Conference on Artificial Intelligence 40 - 46 2004 [Refereed][Not invited]
  • Rafal Rzepka, Kenji Araki
    Journal of Systemics, Cybernetics and Informatics 2 (3) 50 - 57 2004 [Refereed][Not invited]
     
    As most of us subconsciously feel, it is a great difficulty to create a program which could imitate human's way of thinking. Recently the importance of the relation between expressions "feel", "create" and "way of thinking" used in the previous sentence is being noticed, what gave birth to so called "affective computing". During our experiments within GENTA project, we have observed useful connotations between the common sense information and the emotional information which could be retrieved automatically from the Internet resources. Those observations seem promising for the language and knowledge acquisition and suggested us to investigate the subject, and also to develop some ideas, which could be useful to the researchers in various AI fields. We describe GENTA-related sub-projects and their preliminary experiments.
  • Rafal Rzepka, Kenji Araki, Koji Tochinai
    An International Journal of Computing and Informatics Informatica 27 (2) 205 - 212 2003 [Refereed][Not invited]
     
    Rzepka, R., Araki, K., Tochinai, K.: "Emotional Information Retrieval for a Dialogue Agent" in "Perception and Emotions Based Reasoning" - Special Issue of "Informatica" - An International Journal of Computing and Informatics, Volume 27, Number 2: 205-212 (2003)*
  • Rafal Rzepka
    Discovery Science, Proceedings 2843 460 - 467 1611-3349 2003 [Refereed]
  • Is It Out There? The Perspectives of Emotional Information Retrieval from the Internet Resources
    Rzepka Rafal, Kenji Araki, Koji Tochinai
    Proceedings of the IASTED Artificial Intelligence and Applications Conference 22 - 27 2002/09 [Refereed][Not invited]
  • Rafal Rzepka
  • Rafal Rzepka
    Lecture Notes in Computer Science

MISC

  • DanStoデータセットを用いた大規模言語モデルの否定理解の性能評価
    阿部有紗, ジェプカ・ラファウ, 荒木健治  2024度人工知能学会全国大会  2024/06  [Not refereed][Not invited]
  • 常識知識グラフによる知識拡張プロンプトの有効性
    岡田憩, ジェプカ・ラファウ, 荒木健治  2024度人工知能学会全国大会  2024/06  [Not refereed][Not invited]
  • ヒューリスティックと遺伝的アルゴリズムを用いた自動プロンプトチューニング手法
    進藤稜真, ジェプカラファウ, 竹下昌志, 荒木健治  言語処理学会第30回年次大会(NLP2024), P9-8  2024/03  [Not refereed][Not invited]
  • 日本語徳倫理データセットの開発に向けて:英語データセットの翻訳と日本語データセットの比較
    竹下昌志, 連慎治, ジェプカラファウ, 荒木健治  言語処理学会第30回年次大会(NLP2024), P3-26  2024/03  [Not refereed][Not invited]
  • DanSto - Japanese Dataset of Short Stories for Evaluating Context Understanding
    Rafal Rzepka, Kacper Dudzic, Arisa Abe, Kenji Araki  Technical Report of JSAI Special Interest Group for Artificial General Intelligence  SIG-AGI-026-06-  2024/03  [Not refereed][Not invited]
  • 質問応答データセットによる種差別バイアスの測定に向かって
    竹下昌志, ジェプカ・ラファウ, 荒木健治  人工知能学会 第26回汎用人工知能研究会  2024/03  [Not refereed][Not invited]
  • Influence of Person’s Names on Large Language Model Recommendations in Daily-Life Scenarios
    Huizhong Ji, Rafal Rzepka, Kenji Araki  Technical Reports of the Language And Understanding Symposia Series  2023-  (13)  11  -20  2023/12
  • Web-Based Five Senses Input Simulation Twenty Years Later - in the Era of LLMs
    Rafal Rzepka, Don Divin Anemeta, Kenji Araki  Technical Report of JSAI Special Interest Group for Artificial General Intelligence, SIG-AGI-025-07  2023/11  [Not refereed][Not invited]
  • Mariusz Ziółko, Mariusz Ziółko, Napoleon Waszkiewicz, Wojciech Datka, Karolina Kozłowska, Michał Kucharski, Bartosz Ziółko, Rafał Rzepka, Karol Kamiński  JMIR Preprints  2023/05/24  [Not refereed]
     
    BACKGROUND

    Neurodegenerative and mental disorders significantly affect the manner of speaking, syntax, semantics and specific habits of word choice. Linguistic analysis can detect these disorders.

    OBJECTIVE

    The aim of this study was to examine whether speech analysis can be useful for screening test in neurology and psychiatry, due to the limited number of techniques supporting medical diagnostics in these fields. There is a need for a fast, low-cost method for analysing speech samples provided over the phone or as transcripts over the Internet.

    METHODS

    Comparing lemma frequencies in the control group recordings with lemma frequencies in speech of people diagnosed with dementia or depression allowed us to select lemmas that appear too rarely or too frequently in the speech of people affected by disorders. Moreover, the ratio of the number of lemmas to the number of words is a very good indicator of dementia.

    RESULTS

    For neurodegenerative and mental disorders, linguistic analysis frequently results in a more effective diagnosis than analysis of acoustic features. Linguistic changes are easily detectable in dementia, and less noticeable in depressions. By comparing features of speech samples, it was possible to create a classifier which distinguishes one group from the other. We used linguistic analysis to build a system for providing screening tests. Two methods were used to diagnose dementia. The first method is based on the observation that statements made by people with dementia have lower vocabulary variations. The percentage of lemmas (in relation to the number of words) makes it possible to detect dementia states. The second diagnostic method is based on lemma probabilities. This method was also used in depression screening tests.

    CONCLUSIONS

    By knowing features of speech samples recorded by the subjects and the control group, it is possible to create a classifier which distinguishes one group of recordings from the other. Linguistic changes are easily detectable in dementia, and less noticeable in depression.

    CLINICALTRIAL

    Trial Registration CT03197363; https://clinicaltrials.gov

  • 日本語知識グラフを用いた質問応答システムの構築とグラフサイズの影響の検証
    矢野一樹, ジェプカ ラファウ, 荒木健治  情報処理学会 第255回自然言語処理研究会 NLC  2023/03  [Not refereed][Not invited]
  • JCommonsenseMorality: 常識道徳の理解度評価用日本語データセット
    竹下昌志, ジェプカラファウ, 荒木健治  言語処理学会 第29回年次大会 発表論文集, D2-1  2023/03  [Not refereed][Not invited]
  • 補助文自動生成を用いたBERTによる日本語アスペクトベース感情分析におけるアスペクトカテゴリ検出の精度向上
    張懿陽, 竹下昌志, ジェプカラファウ, 荒木健治  言語処理学会 第29回年次大会 発表論文集, A3-2  2023/03  [Not refereed][Not invited]
  • BERTモデルと補助文自動生成に基づいた日本語アスペクトベース感情分析の精度向上
    張 懿陽, ジェプカ・ラファウ, 荒木健治  人工知能学会第2種研究会 ことば工学研究会資料, SIG-LSE-C302-3  2022/12  [Not refereed][Not invited]
  • Large Language Models - Sophisticated Autocomplete Tools or a Big Step Towards AGI?
    Rafal Rzepka  Third Wave of AGI Workshop panel at the 22th SIG-AGI Symposium  2022/11  [Not refereed][Invited]
  • 辞書型感情分析システムの網羅性問題を解決するためのゼロショットテキスト分類モデルの導入とその精度検証
    池野 奏介, ジェプカ ラファウ, 荒木 健治  令和4年度 電気・情報関係学会北海道支部連合大会  2022/10  [Not refereed][Not invited]
  • 竹下昌志, ジェプカラファウ, 荒木健治  言語処理学会 第28回年次大会 発表論文集, A2-2  28th-  2022/03  [Not refereed][Not invited]
  • 勝又友輝, 竹下昌志, ジェプカラファウ, 荒木健治  言語処理学会 第28回年次大会 発表論文集, PH3-2  28th-  2022/03  [Not refereed][Not invited]
  • 坂田将樹, 中山功太, 竹下昌志, ジェプカラファウ, 関根聡, 荒木健治  言語処理学会 第28回年次大会 発表論文集, PH4-6  28th-  2022/03
  • 議論データ抽出における意見とKey Pointのスタンス分類を考慮したMatch Scoringの評価
    桝田大貴, 白藤大幹, ジェプカ・ラファウ, 荒木健治  第12回言語獲得と理解研究会報告  2022/01  [Not refereed][Not invited]
  • 吉井瑞貴, 竹下昌志, RZEPKA Rafal, 荒木健治  人工知能学会第2種研究会ことば工学研究会資料  67th-  2021/11  [Not refereed][Not invited]
  • 議論データ抽出におけるArgumentとKey Pointのスタンス分類からMatch Scoringまでの自動化に向けて
    人工知能学会第, 種研究会, ことば工学研究会資料, SIG-LSE  2021/11  [Not refereed][Not invited]
  • 吉井瑞貴, 竹下昌志, RZEPKA Rafal, 荒木健治  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM)  2021-  2021/11  [Not refereed][Not invited]
  • Wikipedia記事の構造化における関連知識を含む質問の有効性検証
    坂田将樹, Rafal Rzepka, 荒木健治  YANS 2021  2021/08  [Not refereed]
  • 竹下 昌志, ジェプカ ラファウ, 荒木 健治  言語処理学会第２７回年次大会発表論文集  27th-  2021/03  [Not refereed]
  • Cost-Friendly Feature-based Approach for Paraphrase Identification
    Xiaodong Liu, Rzepka Rafal, Araki Kenji  音声言語および自然言語処理シンポジウム  2020/12  [Not refereed]
  • 竹下昌志, RZEPKA Rafal, 荒木健治  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM)  2020-  2020/11  [Not refereed]
  • Mapping arguments to key point: Match Scoring of arguments using sentence embedding and MoverScore without labelled data
    白藤大幹, Rafal Rzepka, 荒木健治  ARG Web インテリジェンスとインタラクション研究会第16回研究会予稿集  2020/10  [Not refereed]
  • Unsupervised summarization of arguments toward key point generation with Sentence-BERT-based method
    白藤大幹, Rafal Rzepka, 荒木健治  ARG Web インテリジェンスとインタラクション研究会第16回研究会予稿集  2020/10  [Not refereed]
  • Language Model-based Context Augmentation for World Knowledge Bases
    Rafal Rzepka, Sho Takishita, Kenji Araki  Proceedings The 34th Annual Conference of the Japanese Society for Artificial Intelligence  2020/06  [Not refereed]
  • Bacteria Lingualis on BERToids - Concept Expansion for Cognitive Architectures
    Rafal Rzepka, Sho Takishita, Kenji Araki  Technical Report of JSAI Special Interest Group for Artificial General Intelligence  SIG-AGI-014-10-  2020/02
  • コーパス作成における専門性を考慮した作業者割当ての提案と化学分野での評価
    吉川和, 金子貴美, 岩倉友哉, 吉田宏章, 熊野康孝, 嶋田和孝, Rafal Rzepka, Patrycja Swieczkowska  言語処理学会 第25回年次大会 発表論文集  B4-3-  2019/03  [Not refereed]
  • 単語の分散表現を用いた日本語イベント連鎖の自動構築
    瀧下祥, Rafal Rzepka, 荒木健治  言語処理学会第25回年次大会(NLP2019)  2019/03  [Not refereed][Not invited]
  • Unicorn Story Generation and Limits of Words ― On Perspectives of Automatic Tacit Knowledge Addition
    Rafal Rzepka, Sho Takishita, Kenji Araki  Technical Report of JSAI Special Interest Group for Artificial General Intelligence, SIG-AGI  2019/03  [Not refereed][Not invited]
  • Retrieving Metaphorical Sentences from Japanese Literature Using Standard Text Classification Methods
    Mateusz Babieno, Sho Takishita, Rafal Rzepka, Kenji Araki  人工知能学会第2種研究会 ことば工学研究会資料, SIG-LSE  2018/10  [Not refereed][Not invited]
  • Towards a Dialog System Supporting Determination of Sensitive Technologies
    Akihiko Obayashi, Rafal Rzepka  The 16th Annual Meeting of Japan Society for Intellectual Production  2018/06  [Not refereed][Not invited]
  • Preliminary Analysis of Weibo Emojis for Sentiment Analysis of Chinese Social Media
    Da Li, Rafal Rzepka, Kenji Araki  Proceedings The 32th Annual Conference of the Japanese Society for Artificial Intelligence  1J3-  (04)  2018/06  [Not refereed][Not invited]
  • First Trials with Culture-Dependent Moral Commonsense Acquisition
    Rafal Rzepka, Li Da, Kenji Araki  Proceedings The 32th Annual Conference of the Japanese Society for Artificial Intelligence  1F2-OS-  (5a-05)  2018/06  [Not refereed][Not invited]
  • 共起頻度と意味的特徴を用いた五感に関する知識の一般性評価手法
    三橋 奎太, ジェプカ・ラファウ, 荒木 健治  ARG Web インテリジェンスとインタラクション研究会第１１回研究会予稿集  85  -90  2017/12  [Not refereed][Not invited]
  • Ptaszynski, Michal, Dybala, Pawel, Higuchi, Shinsuke, Rzepka, Rafal, Araki, Kenji  Proceedings of the 2008 International Conference on Intelligent Agents, Web Technologies & Internet Commerce (IAWTIC'08)  901  -906  2017/01/16
  • 書籍レビューテキストから生成した評価軸とトピックモデルを用いたハイブリット推薦手法の有効性
    北原 將平, ジェプカ ラファウ, 荒木 健治  情報処理学会第１６４回データベースシステム研究会予稿集  2017-DBS-164-  (4)  1  -6  2017/01  [Not refereed][Not invited]
  • Influence of Emoticons and Adverbs on Affective Perception of Japanese Texts
    Rafal Rzepka, Urszula Jagla, Pawel Dybala, Kenji Araki  The 30th Annual Conference of the Japanese Society for Artificial IntelligenceThe 30th Annual Conference of the Japanese Society for Artificial Intelligence  1K4-3H4-OS-17b-3-  2016/06  [Not refereed][Not invited]
  • Study of Emoticon Selection Process for Future Emoticon Recommendation Systems
    Yuki Urabe, Rafal Rzepka, Kenji Araki  The 30th Annual Conference of the Japanese Society for Artificial Intelligence  3H3-OS-17a-2,-  (17a-2)  2016/06  [Not refereed][Not invited]
  • Effectiveness of Topic Model for Review Texts in Hybrid Model Recommendation Method
    Shouhei Kitahara, Rafal Rzepka, Kenji Araki  The 30th Annual Conference of the Japanese Society for Artificial Intelligence  1K4-OS-06a-4in1-  2016/06  [Not refereed][Not invited]
  • KOMUDA Radoslaw, PTASZYNSKI Michal, RZEPKA Rafal, ARAKI Kenji  日本認知科学会大会発表論文集(CD-ROM)  33rd-  2016
  • Tadashi Tanaka, Rafal Rzepka, Kenji Araki  Proceedings of the 50th Language Sense Engineering SIG conference, SIG-LSE-B502  SIG-LSE-B502-1-  1  -6  2015/12  [Not refereed][Not invited]
  • Kohei Matsumoto, Rafal Rzepka, Kenji Araki  Proceedings of the 50th Language Sense Engineering SIG conference, SIG-LSE-B502  SIG-LSE-B502-3,-  17  -26  2015/12  [Not refereed][Not invited]
  • 伝住 颯夏, ジェプカ ラファウ, 荒木 健治  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  49-  37  -42  2015/09/25
  • 書籍の季節推定手法における季語の有効性
    伝住 颯夏, ジェプカ ラファウ, 荒木 健治  人工知能学会第2種研究会 ことば工学研究会資料  SIG-LSE-B501-  36  -41  2015/09  [Not refereed][Not invited]
  • Radiobots型対話システムの提案
    木村 泰知, ジェプカ ラファウ, 高丸 圭一  JSAI2015  2L4-OS-07a-2  2015/05  [Not refereed][Not invited]
  • Extracting ConceptNet Knowledge Triplets from Japanese Wikipedia
    Krawczyk Marek, Rafal Rzepka, Kenji Araki  言語処理学会第21回年次大会予稿集  1052  -1055  2015/03  [Not refereed][Not invited]
  • Performance Evaluation of Season Estimation System Using Seasonal Haiku Indicators
    Satsuna Denzumi, Rafal Rzepka, Kenji Araki  言語処理学会第21回年次大会予稿集  36  -39  2015/03  [Not refereed][Not invited]
  • 井上 育美, 棟方 渚, Rzepka Rafal, 荒木 健治  電子情報通信学会技術研究報告 = IEICE technical report : 信学技報  114-  (73)  63  -70  2014/06/06  
    近年スマートフォンの普及に伴い,携帯端末の文字入力はタッチパネル上に表示されるソフトキーボードが主流となりつつある.しかし,その精度や速度,打ちやすさなどは改善の余地がある.その要因の一つとして,現在の入力手法の多くが主に1指を用いて特定の狭い入力領域をタップすることにあると考えられる.そこで,3指の認識と各指の圧力を用いたタップおよびフリック入力を用いる実験環境を構築し,精度を向上させることを目的としたユーザ評価を試みた.具体的には各指の動作に仮名文字を割り当てるマッピングを行うため,ユーザの打ちやすい指の動きについて検証を行った.本稿では,被験者実験により3指の認識精度と各指の組み合わせによる正答率の評価,使用感アンケートによる印象調査およびそれらの相関について考察を述べる.
  • 井上 育美, 棟方 渚, ジェプカ ラファウ, 荒木 健治  情報処理学会エンタティンメントコンピューティング研究会資料  2014-EC-32-  1  -6  2014/06  [Not refereed][Not invited]
  • Naoya Arakawa, Rafal Rzepka, Hokkaido University  人工知能 = journal of the Japanese Society for Artificial Intelligence  29-  (3)  239  -240  2014/05/01
  • 卜部有記, ジェプカ・ラファウ, 荒木健治  言語処理学会第２０回年次大会(NLP2014)講演論文集  2014/03  [Not refereed][Not invited]
  • PTASZYNSKI Michal, MASUI Fumito, RZEPKA Rafal, ARAKI Kenji  言語処理学会年次大会発表論文集(Web)  20th-  2014
  • 滝澤 満, ジェプカ ラファウ, 荒木 健治  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  44-  13  -18  2013/11/29  [Not refereed][Not invited]
  • 卜部 有記, ジェプカ ラファウ, 荒木 健治  ファジィシステムシンポジウム講演論文集  29-  768  -773  2013/09/09
  • 滝澤満, RZEPKA Rafal, 荒木健治  ファジィシステムシンポジウム講演論文集(CD-ROM)  29-  774  -777  2013/09/09  [Not refereed][Not invited]
  • Rzepka, Rafal, Higuchi, Shinsuke, Ptaszynski, Michal, Araki, Kenji  2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)  2172  -2176  2013/08
  • KRAWCZYK Marek, URABE Yuki, RZEPKA Rafal, ARAKI Kenji  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  43-  47  -54  2013/07/26  
    In this paper, we present A-dur, a tool for calculating duration of actions expressed in Japaneselanguage. A-dur was created in order to be implemented into emotional and social consequences textmining system. However it potentially can be used as a stand-alone time distance calculation solution.It utilizes an extendable temporal expression rules database handcrafted to the system's specifications.We also present the results of our preliminary experiments estimating the database accuracy.
  • Rzepka Rafal, Araki Kenji  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  43-  25  -33  2013/07/26  
    A decade has passed from our paper in which we questionthe sensors technology not being ready to give a machine the five senseswith the recognition level close to human's and, as we expected, sensingdevices are still not sufficient to become a base for the world knowledgebased reasoning. Although we did not developed the proposed systemany further because of limited access to Internet data, the need for machineexperience is now even more urgent than before. Existing worldknowledge retrieval systems tend more and more to concentrate on nameentityinformation ignoring very basic world knowledge which is still oneof the most difficult obstacles on the path for reasoning about everydaysituations. In our paper we will introduce results of our latest trial toautomatize previously proposed theory for enriching machine's capabilityto simulate environmental perception and deepen human behaviorunderstanding.
  • 天谷 祐介, ジェプカ ラファウ, 荒木 健治  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  43-  63  -69  2013/07/26
  • 北嶋志保, RZEPKA Rafal, 荒木健治  人工知能学会第2種研究会ことば工学研究会資料  43rd-  19  -23  2013/07/26  [Not refereed][Not invited]
  • 新田大征, 桝井文人, PTASZYNSKI Michal, 木村泰知, RZEPKA Rafal, 荒木健治  人工知能学会全国大会論文集(CD-ROM)  27th-  ROMBUNNO.203-9IN  -4  2013  [Not refereed][Not invited]
  • ラファウ ジェプカ, 阿部 明典  Journal of Japanese Society for Artificial Intelligence  27-  (6)  673  -673  2012/11/01  [Not refereed][Invited]
  • 天谷 祐介, 荒木 健治, ラファウ ジェプカ  ファジィシステムシンポジウム講演論文集  28-  233  -238  2012/09/12  [Not refereed][Not invited]
  • Kitajima Shiho, Araki Kenji, Rzepka Rafal  Proceedings of the Fuzzy System Symposium  28-  251  -256  2012/09/12  [Not refereed][Not invited]
     
    It is very useful to constructing system that can predict future situation of illness from medical records written by patients in natural language because it will help to ensure informed consent. As the first step, we propose a method to extract description of the effect caused by taking the medicine as a triplet of expressions - medicine name, object of change, and its effect from illness survival blogs. There is no database of evaluation expressions for medical treatment. Moreover usual extraction patterns are not suitable for processing these blogs. Therefore, we decided to extract a expressions triplet using specific clue words and by parsing results. In the experiments, we have confirmed that medication usage information can be extracted with high accuracy compared to other existing methods.
  • Yatsu Motoki, Rzepka Rafal, Araki Kenji  Proceedings of the Fuzzy System Symposium  28-  420  -425  2012/09/12  [Not refereed][Not invited]
     
    In this paper, we propose implicit and explicit utterance generation models and a dialogue system in which such methods are implemented. Modularizing classifiers enables the agent to give input utterance tags of multiple features including types of sentences and mood expressions. The explicit responses are generated if the input text is classified in a specific domain such as Question-Answering, based on the tags given by classifier modules. In the implicit way, the features gotten from the inputted sentences define an agent's internal state. A relativity vector to each domain is sustainably computed based on similarity in Japanese WordNet ontology as the system's internal state. In other cases of the classification, the system generates open-domain utterances. We will discuss the result of experiments intended to show characteristics of both domain detection methods.
  • Rafal Rzepka, Kenji Araki  IPSJ SIG Notes  2012-  (14)  1  -4  2012/07/19  [Not refereed][Not invited]
     
    In this paper we introduce a lexicon of words describing positive and negative consequences of human actions based on Kohlbergian theory of stages that people experience when developing moral reasoning. We briefly introduce our algorithm for an automatic ethical judgment and then describe the role of the data and compare an effectiveness of the polarized set alone and when combined with emotional expressions data used for recognizing possible subjective reactions of average Internet users.
  • PTASZYNSKI MICHAL, RZEPKA RAFAL, ARAKI KENJI, MOMOUCHI YOSHIO  言語処理学会年次大会発表論文集  18th (CD-ROM)-  ROMBUNNO.B5-1  2012/03/13  [Not refereed][Not invited]
  • PTASZYNSKI MICHAL, RZEPKA RAFAL, ARAKI KENJI, MOMOUCHI YOSHIO  言語処理学会年次大会発表論文集  18th (CD-ROM)-  ROMBUNNO.B2-4  2012/03/13  [Not refereed][Not invited]
  • PTASZYNSKI MICHAL, RZEPKA RAFAL, ARAKI KENJI, MOMOUCHI YOSHIO  言語処理学会年次大会発表論文集  18th (CD-ROM)-  ROMBUNNO.F3-6  2012/03/13  [Not refereed][Not invited]
  • Rafal Rzepka, Tyson Roberts, Kenji Araki  言語処理学会年次大会発表論文集  18th (CD-ROM)-  ROMBUNNO.P2-35  2012/03/13  [Not refereed][Not invited]
  • PTASZYNSKI MICHAL, DYBALA PAWEL, RZEPKA RAFAL, ARAKI KENJI, MOMOUCHI YOSHIO  言語処理学会年次大会発表論文集  18th (CD-ROM)-  ROMBUNNO.B2-9  2012/03/13  [Not refereed][Not invited]
  • Dankov Svetoslav, Rzepka Rafal, Araki Kenji  人工知能学会全国大会論文集  26-  1  -3  2012
  • Mazur Michal, Rzepka Rafal, Araki Kenji  人工知能学会全国大会論文集  26-  1  -5  2012
  • Dybala Pawel, Ptaszynski Michal, Rzepka Rafal  人工知能学会全国大会論文集  26-  1  -10  2012
  • DYBALA PAWEL, PTASZYNSKI MICHAL, RZEPKA RAFAL, ARAKI KENJI, SAYAMA KOHICHI  人工知能学会全国大会論文集(CD-ROM)  26th-  ROMBUNNO.3P1-IOS-2A-5  -3P1IOS2a5  2012  [Not refereed][Not invited]
     
    Although we are still quite far from constructing a human-like conversational system, researchers all over the world keep investigating numerous factors that make conversations between humans. In this work we focus on two such factors: humor and metaphors.Numerous research projects exist in the area of metaphor understanding and generation. We propose a unique approach to this subject, based on an observation that humans can not only properly understand and generate metaphors, but also make fun of their misunderstandings. For instance, an utterance "you have legs like a deer" can be understood as a compliment ("long and graceful"), as well as an insult ("very hairy"). If used properly, such misunderstanding can serve as source of humor in human-computer conversations.Currently we are working on constructing a large scale metaphor conceptual network, in which links between concepts are calculated accordingly to their roles in metaphor understanding. When finished, the network should make it possible for the computer to understand and generate metaphors, and, consequently, also misunderstand (or act as if it misunderstood) them. In this paper we propose a design of a conversational system utilizing these mechanisms. We also consider using emotion-from-text detector to improve aptness of generated misunderstandings.
  • MAZUR MICHAL, RZEPKA RAFAL, ARAKI KENJI  人工知能学会全国大会論文集(CD-ROM)  26th-  ROMBUNNO.3P2-IOS-2B-1  2012  [Not refereed][Not invited]
  • DANKOV SVETOSLAV, RZEPKA RAFAL, ARAKI KENJI  人工知能学会全国大会論文集(CD-ROM)  26th-  ROMBUNNO.3P1-IOS-2A-4  2012  [Not refereed][Not invited]
  • 近村 亮一, ラファウ ジェプカ, 荒木 健治  ことば工学研究会 : 人工知能学会第2種研究会ことば工学研究会資料  39-  (0)  13  -18  2011/11/25  [Not refereed][Not invited]
  • Rafał Rzepka, Kenji Araki  Journal of Japan Society for Fuzzy Theory and Intelligent Informatics  23-  (5)  696  -704  2011/10/15  [Not refereed][Invited]
  • 堂腰裕明, 小山聡, 栗原正仁, プタシンスキ ミハウ, ラファウ ジェプカ, 荒木健治  情報処理北海道シンポジウム講演論文集  2011-  223  -224  2011/10/01  [Not refereed][Not invited]
     
    情報処理北海道シンポジウム2011. 2011年10月1日．北見工業大学アトリウム,北見市.
  • 近村 亮一, ラファウ ジェプカ, 荒木 健治  ファジィシステムシンポジウム講演論文集  27-  112  -115  2011/09/12  [Not refereed][Not invited]
  • 高木 慧佑, ジェプカ ラファウ, 荒木 健治  ファジィシステムシンポジウム講演論文集  27-  916  -919  2011/09/12  [Not refereed][Not invited]
  • YAMADA Hiroyuki, RZEPKA Rafal, ARAKI Kenji  IEICE technical report  110-  (400)  59  -64  2011/01/20  
    In this paper we describe our system for retrieving texts using predicate argument structure analysis for queries in natural language. A dependency relation included in a query decides a word score, which is retrieved by defining "retrieval subject" that further becomes the main clue for retrieving sentences using a word classified by predicate argument structure analysis tool SynCha as "-ga case". As a result, the problem of biased word score can be corrected. In this research we target judicial precedents of traffic accidents and use "Oshiete! goo" online QA community entries for experiments.
  • Blog記事からのWeb検索を用いた俳句の自動生成手法における音響及び画像の有効性
    人工知能学会第2種研究会 ことば工学研究会資料  SIG-LSE-C001-  31  -36  2011  [Not refereed][Not invited]
  • PTASZYNSKI Michal, PTASZYNSKI Michal, RZEPKA Rafal, ARAKI Kenji, MOMOUCHI Yoshio  言語処理学会年次大会発表論文集  17th (CD-ROM)-  2011
  • Interactive augmented reality games for knowledge acquisition
    Svetoslav Dankov, Rafal Rzepka, Kenji Araki  第４回楽天シンポジウム2011講演論文集  2011  [Not refereed][Not invited]
  • 第27回ファジィシステムシンポジウム講演論文集  2011  [Not refereed][Not invited]
  • 自動生成された常識的知識を表現する文の自然性判定
    第10回情報科学技術フォーラム(FIT2011)講演論文集  2011  [Not refereed][Not invited]
  • Research on Emoticons: Review of the Field and Proposalof Research Framework
    言語処理学会第17回年次大会(NLP2011)発表論文集  2011  [Not refereed][Not invited]
  • SPEC - Sentence Pattern Extraction and Analysis Architecture
    Michal Ptaszynski, Rafal Rzepka, Kenji Araki, Yoshio Momouchi  言語処理学会第17回年次大会(NLP2011)発表論文集  2011  [Not refereed][Not invited]
  • Michal Ptaszynski, Pawel Dybala, Tatsuaki Matsuba, Fumito Masui, Rafal Rzepka, Kenji Araki  Proceedings of the 1st International Symposium on Linguistic and Cognitive Approaches to Dialog Agents - A Symposium at the AISB 2010 Convention  7  -16  2010/12/01  [Not refereed][Not invited]
     
    Online security has been an important issue for several years. One of the burning online security problems lately in Japan has been online slandering and bullying, which appear on unofficial Web sites. The problem has been becoming especially urgent on unofficial Web sites of Japanese schools. School personnel and members of Parent-Teacher Association (PTA) have started Online Patrol to spot Web sites and blogs containing such inappropriate contents. However, countless number of such data makes the job an uphill task. This paper presents a research aiming to develop a systematic approach to Online Patrol by automatically spotting suspicious entries and reporting them to PTA members and therefore help them do their job. We present some of the first results of analysis of the inappropriate data collected from unofficial school Web sites. The analysis is performed firstly with an SVM based machine learning method to detect the inappropriate entries. After analysis of the results we perform another analysis of the data, using an affect analysis system to find out how the machine learning model could be improved.
  • 複数の対話システムからの応答候補文を用いた最適応答文選択手法の性能評価
    情報処理学会研究報告  2010-NL-195(10)-  1  -7  2010  [Not refereed][Not invited]
  • 対話システムにおける対話履歴要約の有効性について
    福田 彩子, 荒木 健治, ジェプカ ラファウ  情報処理学会研究報告  2010-NL-195(11)-  1  -6  2010  [Not refereed][Not invited]
  • PUNDA Numbears: Proposal of Goroawase Generating System for Japanese
    言語処理学会第16回年次大会(NLP2010)発表論文集  345  -348  2010  [Not refereed][Not invited]
  • Web検索と単語n-gramモデルによる対話処理手法における補佐システムの有効性
    人工知能学会第2種研究会 ことば工学研究会資料  SIG-LSE-B001-  17  -23  2010  [Not refereed][Not invited]
  • WordNet及びWeb検索による常識的知識ベースのための文生成手法
    村本晃一, ジェプカ・ラファウ, 荒木健治  人工知能学会第2種研究会 ことば工学研究会資料  SIG-LSE-B001-  1  -7  2010  [Not refereed][Not invited]
  • On the Need of Context Processing in Affective Computing
    Michal Ptaszynski, Rafal Rzepka, Kenji Araki  Proceedings of the 26th Fuzzy System Symposium (FSS2010)  920  -924  2010  [Not refereed][Not invited]
  • Webデータを用いた比喩的説明文生成の研究
    近村 亮一, ジェプカ ラファウ, 荒木 健治  平成２２年度電気・情報関係学会北海道支部連合大会講演予稿集  2010  [Not refereed][Not invited]
  • Towards Fully Automatic Emoticon Analysis System (^o^)
    Michal Ptaszynski Pawel, Dybala Rafal Rzepka, Kenji Araki  言語処理学会第16回年次大会(NLP2010)発表論文集  2010  [Not refereed][Not invited]
  • Web検索と単語n-gramモデルを用いた文生成手法の性能評価
    高橋瑞希, Rafal Rzepka, 荒木健治  言語処理学会第16回年次大会(NLP2010)発表論文集  2010  [Not refereed][Not invited]
  • 長谷川大, ジェプカ ラファウ, 荒木健治  言語処理学会年次大会発表論文集  15th-  509  -511  2009/03/02  [Not refereed][Not invited]
  • 対話文生成のためのWebを用いた話題語の抽出
    下川尚亮, ジェプカ・ラファウ, 荒木健治  情報処理学会研究報告  2009-NL-189-  121  -126  2009  [Not refereed][Not invited]
  • 非タスク指向型対話システムにおける非同期並列的相互作用による言語獲得手法の提案
    若原 基, ジェプカ ラファウ, 荒木 健治  電子情報通信学会 第二種研究会資料  WI2-2009-28-45-  53  -54  2009  [Not refereed][Not invited]
  • 非同期型チャットアプリケーションに対応した雑談システムの構築と評価
    若原 基, ジェプカ ラファウ, 荒木 健治  人工知能学会第2種研究会 ことば工学研究会資料  SIG-LSE-A902-  19  -26  2009  [Not refereed][Not invited]
  • 対話システムのための因果関係知識を含む文からの関連語抽出
    藤田 基靖, ジェプカ ラファウ, 荒木健治  情報処理北海道シンポジウム 2009講演論文集  2009  [Not refereed][Not invited]
  • 単語n-gramモデルを用いた文生成手法の改善案
    高橋 瑞希, ジェプカ ラファウ, 荒木健治  平成２１年度 電気・情報関係学会北海道支部連合大会講演論文集  2009  [Not refereed][Not invited]
  • 複数文からなる質問を想定した交通事故判例検索の検討
    山田浩之, 木村泰知, ジェプカ・ラファウ, 荒木健治  情報処理北海道シンポジウム 2009講演論文集  2009  [Not refereed][Not invited]
  • ジェプカ ラファウ, 桝井 文人, 荒木 健治  2009年度人工知能学会全国大会（第２３回）論文集(CD-ROM)  23-  1  -4  2009  [Not refereed][Not invited]
  • 長谷川 大, ジェプカ ラファウ, 荒木 健治  2009度人工知能学会全国大会（第23回）  23-  1  -3  2009  [Refereed][Not invited]
  • When Should Computers Joke? - Concept of Emotiveness Analysis Based Timing Algorithm for Humor-Equipped Conversational Systems
    Pawel Dybala Michal, Ptaszynski Rafal Rzepka, Kenji Araki  言語処理学会第15回年次大会(NLP2009)発表論文集（CD-ROM）  2009  [Not refereed][Not invited]
  • PTASZYNSKI Michal  Proceedings of The Fifteenth Annual Meeting of The Association for Natural Language Processing (NLP2009)  825  -828  2009  [Not refereed][Not invited]
  • Web上のテキストデータを用いた非タスク指向型対話システムのための係り受け解析による連想メカニズムの構築
    若原 基, ジェプカ ラファウ, 荒木 健治  電子情報通信学会 情報・システムソサイエティ総合大会特別号  2009  [Not refereed][Not invited]
  • DANKOV Svetoslav, RZEPKA Rafal, ARAKI Kenji  IPSJ SIG Notes  2008-  (113)  105  -111  2008/11/19  [Not refereed][Not invited]
     
    In this paper we present and build on a novel approach for automatically collecting common sense statements from the World Wide Web. As a backbone of our method we use generic rules and contextual clues to identify potential candidates. The generic rules consist of predetermined grammatical rules used to express common sense. The contextual clues consist of syntactic and semantic clues. The syntactic clues are represented by various syntactic structures frequently seen around common sense statements, while the semantic clues are represented by the various relationships between entities in the statement. To query for semantic relationships we are using WordNet. Two experiments were performed, evaluating the performance of our method, evaluating the viability of using semantic clues (WordNet) as well as the performance of our method when applied in another language (Bulgarian).
  • Takamaru Keiichi, Shibuki Hideyuki, Kimura Yasutomo, Matsuhara Masafumi, Rzepka Rafal, Murakami Koji  情報科学技術フォーラム講演論文集  7-  (2)  233  -234  2008/08/20  [Not refereed][Not invited]
     
    第7回情報科学技術フォーラム(FIT2008). 2008年9月2日～4日．慶應義塾大学 湘南藤沢キャンパス,藤沢市
  • MATSUHARA Masafumi, KIMURA Yasutomo, SHIBUKI Hideyuki, TAKAMARU Keiichi, RZEPKA Rafal, MURAKAMI Koji  情報科学技術フォーラム講演論文集  7-  (2)  235  -236  2008/08/20  [Not refereed][Not invited]
     
    第7回情報科学技術フォーラム(FIT2008). 2008年9月2日～4日．慶應義塾大学 湘南藤沢キャンパス,藤沢市.
  • KIMURA Yasutomo, SHIBUKI Hideyuki, TAKAMARU Keiichi, MATSUHARA Masafumi, RZEPKA Rafal, MURAKAMI Koji  情報科学技術フォーラム講演論文集  7-  (2)  237  -238  2008/08/20  [Not refereed][Not invited]
     
    第7回情報科学技術フォーラム(FIT2008). 2008年9月2日～4日．慶應義塾大学 湘南藤沢キャンパス,藤沢市.
  • SHI Wenhan, PTASZYNSKI Michal, RAFAL Rzepka, ARAKI Kenji  IEICE technical report  TL-2008-49-  (353)  31  -34  2008  [Not refereed][Not invited]
     
    In the last few years there has been a rapid development in computerisation. Human lives depend on computers more and more by the day. To make the communication with the machines more natural for an average user, one of the crucial issues is to develop in machines applications and interfaces for understanding human emotions. In this paper we propose a system for affect analysis of textual utterances in Japanese. The emotional states are determined on the basis of appearance of emotive elements and emotive expressions in the utterances as well as Web mining technique in which emotional associations are extracted from the Web using causality features.
  • 松原 雅文, 木村 泰知, 渋木 英潔, 高丸 圭一, RZEPKA Rafal, 村上 浩司  人工知能学会全国大会論文集  8-  (0)  406  -406  2008  [Not refereed][Not invited]
     
    裁判員制度に向けて、刑事事件の特徴と量刑の対応付けを行い、その関連性の可視化を行うことで、量刑判断の指標を提示するシステムの構築を目指す。本発表では、98年から99年の毎日新聞の記事を対象とする。
  • 複数対話システムの応答候補からの最適な応答の選択手法
    今井 健太, ジェプカ ラファウ, 荒木 健治  平成２０年度 電気・情報関係学会北海道支部連合大会講演論文集  2008  [Not refereed][Not invited]
  • ウェブ上の因果関係を用いたユーザ入力文からの感情情報の推測
    施,文翰, ジェプカ ラファウ, 荒木 健治  第７回情報技術フォーラム(FIT2008)講演論文集  2008  [Not refereed][Not invited]
  • PTASZYNSKI Michal, DYBALA Pawel, RZEPKA Rafal  人工知能学会第22回全国大会(JSAI2008)講演論文集  22-  1  -4  2008  [Not refereed][Not invited]
  • Commonsense and context: a novel approach for automatic extraction of generic statement
    人工知能学会第22回全国大会(JSAI2008)講演論文集  2008  [Not refereed][Not invited]
  • Webを利用した連想単語及びモダリティ表現による雑談システム
    言語処理学会第14回年次大会(NLP2008)発表論文集（CD-ROM）  2008  [Not refereed][Not invited]
  • PTASZYNSKI Michal  Proceedings of The Fourteenth Annual Meeting of The Association for Natural Language Processing, 2008  171  -174  2008  [Not refereed][Not invited]
  • Dajare Generating Support Tool - Towards Applicable Linguistic Humor Processing
    言語処理学会第14回年次大会(NLP2008)発表論文集（CD-ROM）  2008  [Not refereed][Not invited]
  • PTASZYNSKI Michal, DYBALA Pawel, SHI Wen Han, RZEPKA Rafal, ARAKI Kenji  映像情報メディア学会技術報告  31-  (47(ME2007 195-214))  2007
  • PTASZYNSKI Michal, SAYAMA Koichi, RZEPKA Rafal, ARAKI Kenji  電気・情報関係学会北海道支部連合大会講演論文集(CD-ROM)  2007-  2007
  • Raghavacharya Hari, Rzepka Rafal, Araki Kenji  人工知能学会第20回全国大会(JSAI2006)(CD-ROM)  20-  1  -4  2006  [Not refereed][Not invited]
  • Towards Systems Using WWW to Presume What Could and Would Have Happened
    人工知能学会第20回全国大会(JSAI2006)(CD-ROM)  2006  [Not refereed][Not invited]
  • Rafal Rzepka, Kenji Araki  Proceedings of the 19th Annual Conference of the Japanese Society for Artificial Intelligence  1B1-08  2005  [Not refereed][Not invited]
  • Turing Test During Olympics and Minsky's Anger - Some Pros and Cons of Dialog Systems Competition
    言語処理学会第１１回年次大会(NLP2005)ワークショップ「評価型ワークショップを考える」  2005  [Not refereed][Not invited]
  • RZEPKA Rafal, ITOH Toshihiko, ARAKI Kenji  IPSJ SIG Notes  2004-  (73)  11  -18  2004/07/15  
    In this paper we introduce some ideas for reusing cognitive science concepts which realizing before was impossible due to the technical limits. We concentrate on the Schankian scripts which could help to build plans as the basic method for achieving goals. In contradistinction to the authors of classic cognitivistic ideas, we can currently use powerful computers and terabytes of data which could help to make their concepts usable in not restricted domains for any kind of application using commonsense knowledge. Many useful projects were abandoned because of difficulties due to the manual input of big sets of data. We plan to build a commonsense processing systems which retrieves commonsensical data from the WWW resources. This paper introduces the theoretical side of our research with some results of preliminary tests.
  • Five Senses Input Simulation Based on Web Resources
    人工知能学会研究会資料  SIG-LSE-A301-  65  -73  2004  [Not refereed][Not invited]
  • ジェプカラファウ  第18回人工知能学会全国大会, 金沢, 2004  2004
  • Does the Commonsense Depend on Culture? Results of Simple Scripts Retrieval from WWW
    平成16年度電気・情報関係学会北海道支部連合大会講演論文集(IEEEオーガナイズドセッション)  2004  [Not refereed][Not invited]
  • RZEPKA R.  2002 IEEE Hokkaido Chapters Joint Convention Record, 2002  229  -230  2002  [Not refereed][Not invited]
  • Rzepka Rafal, Araki Kenji, Tochinai Koji  人工知能学会全国大会論文集  16-  1  -3  2002  [Not refereed][Not invited]
  • RZEPKA R.  2001 IEEE Hokkaido Chapters Joint Convention Record, 2001  414  -415  2001  [Not refereed][Not invited]

Books etc

  • Rafal Rzepka, Kenji Araki (Joint work)
    Elsevier 2024/02 (ISBN: 9780443159916)
  • Corpus Linguistics and Discourse Annotations. International Journal of Computational Linguistics and Chinese Language Processing (IJCLCLP)
    (Joint editor)
    2022/08
  • The 7th Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2021) - CEUR Workshop Proceedings vol. 2935
    Rafal Rzepka, Jordi Vallverdú, Andre Wlodarczyk, Michal Ptaszynski, Pawel Dybala (Editor編集長)
    2021/08
  • Advances in Intelligent Systems and Computing
    Yasufumi Takama, Naohiro Matsumura, Katsutoshi Yada, Mitsunori Matsushita, Daisuke Katagami, Akinori Abe, Hisashi Kashima, Toshihiro Hiraoka, Takahiro Uchiya, Rafal Rzepka (Joint editor)
    Springer 2021/06 (ISBN: 9783030964504)
  • Joint Proceedings of the Workshops on Linguistic and Cognitive Approaches to Dialog Agents (LaCATODA 2019) and on Bridging the Gap Between Human and Automated Reasoning (BtG 2019) CEUR Workshop Proceedings vol. 2935
    Ulrich Furbach, Steffen Hoelldobler, Marco Ragni, Rafal Rzepka, Claudia Schon, Jordi Vallverdu, Andre Wlodarczyk (Joint editorGuest Editor)
    2019/08
  • Proceedings of the Linguistic and Cognitive Approaches To Dialog Agents Workshop co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI 2018)
    Rafal Rzepka, Jordi Vallverdú, Andre Wlodarczyk (Joint editorInternational)
    CEUR-WS 2018/09
  • Artificial General Intelligence - 11th International Conference, AGI 2018
    Matthew Iklé, Arthur Franz, Rafal Rzepka, Ben Goertzel (Joint editorInternational)
    Springer 2018/08
  • AI with Empathy: language, emotions, ethics, humor, common sense
    Rafal RZEPKA (Joint workcommon sense, machine ethics)
    Morikita Publishing House 2016/06
  • Rethinking Machine Ethics in the Age of Ubiquitous Technology
    Rafal Rzepka, Kenji Araki (ContributorSemantic Analysis of Bloggers Experiences as a Knowledge Source of Average Human Morality)
    IGI Global, Hershey, PA, USA 2015/05
  • Machine Medical Ethics (Intelligent Systems, Control and Automation: Science and Engineering Series)
    Rafal Rzepka, Kenji Araki (ContributorELIZA Fifty Years Later: An Automatic Therapist Using Bottom-Up and Top-Down Approaches)
    Springer International Publishing 2014/09 (ISBN: 9783319081076) 15 http://link.springer.com/chapter/10.1007%2F978-3-319-08108-3_16 
    Our methods for realizing a moral artificial agent assume that the wisdom of crowds can equip a machine with the enormous number of experiences that are the source of its ethical reasoning. Every second, people with different cultural, religious or social backgrounds share their personal experiences about multitudes of human acts. We propose that a machine therapist capable of analyzing thousands of such cases should be more convincing and effective talking to a patient, instead of analyzing single keywords. In this chapter, we introduce this vision and several techniques already implemented in an algorithm for generating empathic machine reactions based on emotional and social consequences. We show the roles that Bentham’s Felicific Calculus, Kohlberg’s Theory of Stages of Moral Development and McDougall’s classification of instincts play in the agent’s knowledge acquisition, and we describe the accuracy of already working parts. Modules and lexicons of phrases based on these theories enable a medical machine to gather information on how patients typically feel when certain events happen, and what could happen before and after actions. Such empathy is important for understanding the actions of other people, and for learning new skills by imitation. We also discuss why this bottom-up approach should be accompanied by a top-down utility calculation to ensure the best outcome for a particular user, and what ethical dilemmas an advanced artificial therapist could cause.
  • Uma Shanker Tiwary, Uma Shanker Tiwary, Tanveer J. Siddiqui 
    IGI Global 2012/04 (ISBN: 1466609540) 386

Presentations

  • Examples of NLP applications in detecting immoral behavior in Japanese  [Invited]
    Rafal Rzepka
    The Fifth Conference on Information theories and information analysis in forensics, criminology, law and security sciences  2023/06
  • 人工知能の基礎知識  [Invited]
    ジェプカ・ラファウ
    札幌まちづくり「おしゃべりサロン」  2023/03
  • 人工知能と未来を考える  [Invited]
    ジェプカ・ラファウ
    苫小牧市科学センター「サイエンス・カフェ」  2023/01
  • 人工知能と倫理  [Invited]
    ジェプカ・ラファウ
    苫小牧市科学センター「サイエンス・カフェ」  2023/01
  • Large Language Models - Sophisticated Autocomplete Tools or a Big Step Towards AGI?  [Invited]
    Rafal Rzepka
    第22回汎用人工知能研究会  2022/11
  • Mind the Gap - Thinking About Meaning in the Context of Ethical AGI.
    Rafal Rzepka
    Invited Lecture at Indian Institute of Information Technology Kottayam  2022/09
  • Invited Talk - Humanizing Machines by Simulating Empathy and Understanding Human Needs  [Invited]
    Rafal Rzepka
    IJCAI Humanizing AI Workshop  2019/08
  • Invited Talk - "Building Future with AI"  [Invited]
    Rafal Rzepka
    Tomakomai Science Center  2019/06
  • Invited Talk - "AI Ethics for Developers, Users and Legislators - Problems and Solutions"  [Invited]
    Rafal Rzepka
    Nitobe School Leaders Meeting  2019/06
  • Invited lecture - Common Sense Morality for Machines - Are Our Shared Experiences the Only Solution for Safe Artificial Intelligence?  [Invited]
    Rafal RZEPKA
    Morality mod Science Seminar  2018/09
  • Invited Talk - Wisdom of Crowds as The Source of The Human Moral Sense for Artificial Intelligence  [Invited]
    Rafal RZEPKA
    National Information Academy Seminar  2018/08
  • From Signals, through Words, to Safe Behavior - Value Alignment by Human Experience Analysis  [Invited]
    Rafal RZEPKA
    Invited Talk at the 8th International Workshop on Signal Design and its Applications in Communications (IWSDA’17)  2017/09
  • Automatic Text Analysis and Generation - Less and More Serious Applications  [Invited]
    Rafal RZEPKA
    Invited Talk at Jagiellonian University  2015/12
  • Invited Talk - Pros and Cons of Borrowing Morality from P2P Civilization  [Invited]
    Rafal RZEPKA
    AAAI Spring Symposium  2014/03
  • "Artificial Common Sense in a Pill"  [Invited]
    Rafal RZEPKA
    Invited Lecture at Copernicus University  2011/05
  • "Japanese machines know what you feel - about interdisciplinary approach to AI"  [Invited]
    Rafal RZEPKA
    Invited Talk at Adam Mickiewicz University  2010/02
  • "Why does my robot read your blog - about new perspectives for AI in the Internet era"  [Invited]
    Rafal RZEPKA
    Invited Talk at Szczecin Institute of Technology  2010/02

Association Memberships

  • Association for Computational Linguistics (ACL)   AAAI   日本認知科学学会   人工知能学会   Japanese Cognitive Science Society   Japanese Society for Artificial Intelligence   

Works

  • Masashi Takeshita, Rafal Rzepka, Kenji Araki 2023/03
  • Semantic primes prompts in Japanese (approx. 64,000 annotated cognitive knowledge data samples
    Rafal Rzepka 2023
  • ROCStories-jp: approx. 55,000 English stories with alternative endings automatically translated to Japanese
    Rafal Rzepka 2023
  • DanSto (Dangerous Stories) approx. 8,000 five-sentence stories in Japanese based on DanSen dataset
    Rafal Rzepka 2023
  • CISTEC FAQ-based Trade Security QA dataset
    Rafal Rzepka, Akihiko Obayashi 2022/09
  • DanSen (Dangerous Sentences) Dataset
    Rafal Rzepka, Yuki Katsumata 2022/03
  • MLAsk: Open Source Affect Analysis Software for Textual Input in Japanese
    Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki, Fumito Masui 2016/09
  • Japanese Winograd Schemas
    Rafał Rzepka, Soichiro Tanaka, Shiho Kitajima 2015/11
  • Japanese Blog Corpus YACIS
    Michal Ptaszynski, Pawel Dybala, Rafal Rzepka, Kenji Araki, Yoshio Momouchi 2012
  • MuCha - Text Processing Tool using Grammatically Aware Regular Expressions
    Tyson Roberts, Rafal Rzepka 2012/01
  • Japanese Natural Language Toolset MIT ConceptNet
    Tyson Roberts, Rafal Rzepka, Kenji Araki 2010/10

Research Projects

  • 日本学術振興会：科学研究費助成事業
    Date (from‐to) : 2023/04 -2026/03 
    Author : 大林 明彦
  • Japan Society for the Promotion of Science：Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
    Date (from‐to) : 2022/04 -2025/03 
    Author : RZEPKA Rafal
  • AI systems that can be explained in language based on knowledge and reasoning
    JST：CREST
    Date (from‐to) : 2020 -2025 
    Author : Kentaro Inui
  • Japan Society for the Promotion of Science：Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)
    Date (from‐to) : 2020/04 -2023/03 
    Author : Akihiko Obayashi, Rafal Rzepka
     
    During the second year of our project we extended our question-answer database for the conversational expert system for supporting non-expert in deciding if their research or goods for export are require governmental permission. We also created a synonym dataset of chemical substances based on CAS numbers (by hiring a worker with the grant funds) to improve mapping terms in queries to the keywords in our database. This lead to enriching triplets for our knowledge graph which will now contains concepts as "SynonymOf", "HasCAS", "HasDefinition", "AddressHasText" or "IsControlledBy". We experimented with visualizing these concepts to have a better insight into connections among terms used in the Controlled Goods/Technologies Matrix Table. This should improve the process of searching legal texts for answers to the user’s queries. We published our findings from experiments with question answering and presented them during an international conference. It appeared that traditional and modern learning approaches (LDA-Based Ranker, BERT Ranker, SQuAD model-based QA and GPT-2-based Answer Generation) achieved worse results than keyword matching based on the glossary published as an index for the legal text regarding trade security issues. This convinced us to shift the weight from automatic to half-automatic in our approach for searching paragraphs related to user queries. However, there is still a necessity for using similarity calculation in order to deal with terms which are used in queries but are not included in the glossary used for keyword matching.
  • Development of a System for Collecting Context Data for Large-Scale Inverse Reinforcement Learning
    JSPS：Grant-in-Aid for Scientific Research (C)
    Date (from‐to) : 2017/04 -2022/03 
    Author : Rafal RZEPKA
  • Research on Text Generation
    Fujitsu Ltd：
    Date (from‐to) : 2021 -2022 
    Author : ジェプカ・ラファウ, 岩倉 友哉, 馬 春鵬
  • Research on chemical substance information extraction technology for creating chemical knowledge base
    Fujitsu Labs：
    Date (from‐to) : 2019 -2020 
    Author : ジェプカ・ラファウ, Patrycja Swieczkowska, 岩倉 友哉, 渡邊 大貴
  • Investigation of Communication Development Stages and Conversational Characteristics for Foreign Language Tutoring Systems
    Hokkaido University：IST Grant for Research Fellowships for Young Researchers
    Date (from‐to) : 2016 -2017 
    Author : Rafal Rzepka
  • Methods For an Automatic Generation of MIT ConceptNet Japanese Entries
    Hokkaido University：IST Grant For Young Researchers
    Date (from‐to) : 2014 -2015 
    Author : Rafal Rzepka
  • 世評・感情・倫理を考慮して柔軟に有害表現を検出する技術の開発と学校非公式サイト監視への応用
    JSPS：Kakenhi Grant
    Date (from‐to) : 2012/04 -2014/03 
    Author : Fumito MASUI
  • ウェブマイニングによる人間行動の倫理性推論アルゴリズムの構築
    JSPS：Kaken Grant
    Date (from‐to) : 2012/04 -2014/03 
    Author : RZEPKA Rafal
  • Applying Common Sense Retrieved from the Web to a Housekeeping Robot
    Hokkaido University：IST Grant For Young Researchers
    Date (from‐to) : 2010 -2012 
    Author : Rafal Rzepka
  • 情報の遷移にダイナミックに追従するインターネット単語帳システムの開発
    JSPS：Kakenhi Grant
    Date (from‐to) : 2008/04 -2011/03 
    Author : Fumito Masui
  • Automatic Retrieval of Elementary Knowledge for Nano-electronics
    JSPS：グローバルCOE「知の創出を支える次世代IT基盤拠点」
    Date (from‐to) : 2007 -2009 
    Author : Kazuhisa Sueoka
  • Communication Ability Development Toy with Common Sense Dialogue System
    Nissan Foundation：
    Date (from‐to) : 2005/04 -2008/03 
    Author : Rafal Rzepka

Social Contribution

  • 人工知能の基礎知識
    Date (from-to) : 2023/03/06-2023/03/06
    Role : Lecturer
    Sponser, Organizer, Publisher  : 札幌まちづくり
    Event, Program, Title : 「おしゃべりサロン」
  • 人工知能と倫理
    Date (from-to) : 2023/01/21-2023/01/21
    Role : Lecturer
    Sponser, Organizer, Publisher  : 苫小牧市科学センター
    Event, Program, Title : 「サイエンス・カフェ」
  • 人工知能と未来を考える
    Date (from-to) : 2023/01/21-2023/01/21
    Role : Lecturer
    Sponser, Organizer, Publisher  : 苫小牧市科学センター
    Event, Program, Title : 「サイエンス・カフェ」
  • AIと未来を創る ～AIの歴史と可能性～
    Date (from-to) : 2019/06/29-2019/06/29
    Role : Appearance
    Sponser, Organizer, Publisher  : 苫小牧科学センター
  • 「親子でケイケン値UP」 ～ポーランドで育って 日本で育てて～
    Date (from-to) : 2013/11/06-2013/11/06
    Role : Panelist
    Sponser, Organizer, Publisher  : 東区PTA連合会
    Event, Program, Title : 研修大会

Media Coverage

  • 常識的知識や倫理AIで判断
    Date : 2018/11/06
    Program, newspaper magazine: 北海道新聞
    Paper
  • Let the machine judge us
    Date : 2015/09
    Writer: Other than myself
    Publisher, broadcasting station: Tygodnik Powszechny
    Paper
  • The vacuum cleaner that can tweet it can’t fly
    Date : 2014/07
    Writer: Other than myself
    Publisher, broadcasting station: 北海道大学
    Program, newspaper magazine: Spotlight on Research
    Pr

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