Knowledge-Intensive Artificial Intelligence (KIAI) discusses the research progress of our team. Currently, the meeting is intended to deeply discuss the current progress of each member rather than to study relevant previous work (still, observers are welcome!). In every meeting, all the members will talk about their research progress in 5 minutes. It is recommended that all the members bring a short note or slides to describe their progress for an active discussion. The schedule is listed below.

Date

1st Semester: Wed 16:20-17:50; 2nd Semester: Wed 16:20-17:50

Members

乾, 井之上, 代勤, Paul, 稲田, 小林 (遠隔), 横井, 高橋, 佐藤, 清野, 赤間, 吉成

Related Keywords

人工知能/Artificial Intelligence, 物語理解/Story Understanding, プラン/Plan, 修辞構造/Rhetorical Structure, 照応/Anaphora, 省略/Ellipsis, ゼロ照応/Zero Anaphora

• Commonsense Reasoning 読み会

Schedule †

Future †

Upcoming †

• 以後 🔒esa に移行

Done †

• Mon 4/10, 17:30-
  • 短いチーム (最大10分で交代)
    • 🔒esa
  • 長いチーム (最大20分で交代)

Past †

• Spring 2016-Winter 2017
• Spring 2015-Winter 2016

Links †

Artificial Intelligence †

• Levesque, Hector J. "On our best behaviour." The 23rd International Joint Conference on Artificial Intelligence (IJCAI). August. 2013. pdf
• Rahman, Altaf, and Vincent Ng. "Resolving complex cases of definite pronouns: the winograd schema challenge." Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2012. pdf dataset dataset 2
• Roemmele, Melissa, Cosmin Adrian Bejan, and Andrew S. Gordon. "Choice of Plausible Alternatives: An Evaluation of Commonsense Causal Reasoning." AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning. 2011. pdf dataset
• Levesque, Hector J., Ernest Davis, and Leora Morgenstern. "The Winograd schema challenge." AAAI Spring Symposium: Logical Formalizations of Commonsense Reasoning. 2011. pdf
• Jerry R. Hobbs, Mark Stickel, Douglas Appelt, and Paul Martin. Interpretation as Abduction, Artificial Intelligence, 1993. pdf

Logical Inference †

• Mohammad Shahed Sorower, Thomas G. Dietterich, Janardhan Rao Doppa Walker Orr, Prasad Tadepalli, and Xiaoli Fern. Inverting Grice’s Maxims to Learn Rules from Natural Language Extractions. NIPS 2011. pdf
• Ekaterina Ovchinnikova, Niloofar Montazeri, Theodore Alexandrov, Jerry R. Hobbs, Michael C. McCord and Rutu Mulkar-Mehta. Abductive Reasoning with a Large Knowledge Base for Discourse Processing. IWCS 2011. pdf
• James Blythe, Jerry R. Hobbs, Pedro Domingos, Rohit J. Kate and Raymond J. Mooney. Implementing Weighted Abduction in Markov Logic. IWCS2011. pdf
• Dan Garrette, Katrin Erk, Raymond Mooney. Integrating Logical Representations with Probabilistic Information using Markov Logic. IWCS2011. pdf
• Sindhu V. Raghavan, Raymond J. Mooney. Bayesian Abductive Logic Programs. AAAI 2010. pdf
• RohitJ. Kate RaymondJ. Mooney. Probabilistic Abduction using Markov Logic Networks. IJCAI 2009 on PAIR 2009. pdf
• Jerry R. Hobbs, Mark Stickel, Douglas Appelt, and Paul Martin. Interpretation as Abduction, Artificial Intelligence, 1993. pdf
• J. Bos (2009): Applying automated deduction to natural language understanding. Journal of Applied Logic 7(1): 100–112. pdf
• A Unified Approach to Abductive Inference (ARO 2008 MURI Project@University of Washington)

Discourse Theory †

• Rhetorical Structure Theory
• Discourse Representation Theory
• J. Bos, M. Nissim (2008): Combining Discourse Representation Theory with FrameNet. In: R. Rossini Favretti (ed): Frames, Corpora, and Knowledge Representation, pp 169–183, Bononia University Press. pdf
• Dan Cristea, Nancy Ide and Laurent Romary. Veins Theory: A Model of Global Discourse Cohesion and Coherence. ACL 1998. pdf
• Barbara J. Grosz, Aravind K. Joshi and Scott Weinstein. Centering: A Framework for Modeling the Local Coherence of Discourse. Computational Linguistics, 1995. pdf
• Barbara J. Grosz and Candace L. Sidner. ATTENTION, INTENTIONS, AND THE STRUCTURE OF DISCOURSE. Computational Linguistics, 1986. pdf

• Bonnie Webber. Accounting for Discourse Relations: Constituency and Dependency. Intelligent Linguistic Architectures, 2006. pdf
• Florian Wolf, Edward Gibson. Representing Discourse Coherence: A Corpus-Based Study. Computational Linguistics, 2005.
• Bonnie Webber, Matthew Stone, Aravind Joshi and Alistair Knott. Anaphora and Discourse Structure. Computational Linguistics, 2003. pdf
• Daniel Marcu. A Formal and Computational Synthesis of Grosz and Sidner's and Mann and Thompson's theories. 1999. pdf
• Erhard Hinrichs. Discourse Annotation of Corpora. pdf
• Johanna D. Moore and Martha E. Pollack. A Problem for RST: The Need for Multi-Level Discourse Analysis. Computational Linguistics, 1992. pdf

Discourse Parsing †

• Alexis Palmer, Afra Alishahi and Caroline Sporleder. Robust Semantic Analysis for Unseen Data in FrameNet. RANLP2011. pdf
• Michaela Regneri, Alexander Koller, Josef Ruppenhofer and Manfred Pinkal. Learning Script Participants from Unlabeled Data. RANLP2011. pdf
• Manfred Klenner and Don Tuggener. An Incremental Entity-Mention Model for Coreference Resolution with Restrictive Antecedent Accessibility. RANLP2011. pdf
• Ziheng Lin, Hwee Tou Ng and Min-Yen Kan. Automatically Evaluating Text Coherence Using Discourse Relation. ACL 2011. pdf
• Ziheng Lin, Hwee Tou Ng, and Min-Yen Kan. A PDTB-Styled End-to-End Discourse Parser. 2010. pdf
• Annie Louis, Rashmi Prasad, Aravind Joshi and Ani Nenkova. Using Entity Features to Classify Implicit Discourse Relations. SIGDIAL 2010. pdf
• Aria Haghighi and Dan Klein. Coreference Resolution in a Modular, Entity-Centered Model. NAACL-HLT 2010. pdf
• Emily Pitler, Annie Louis and Ani Nenkova. Automatic sense prediction for implicit discourse relations in text. ACL-IJCNLP 2009. pdf
• Rajen Subba and Barbara Di Eugenio. An effective Discourse Parser that uses Rich Linguistic Information. NAACL-HLT 2009. pdf
• Ravikiran Vadlapudi, Poornima Malepati and Suman Yelati. Hierarchical Discourse Parsing Based on Similarity Metrics. RANLP 2009. pdf
• Manfred Klenner, Étienne Ailloud. Optimization in Coreference Resolution is not Needed: A Nearly-Optimal Algorithm with Intensional Constraints. EACL 2009. pdf
• Jason Baldridge and Alex Lascarides. Probabilistic Head-Driven Parsing for Discourse Structure. CoNLL 2005. pdf
• Daniel Marcu and Abdessamad Echihabi. An Unsupervised Approach to Recognizing Discourse Relations. ACL 2002. pdf

Plan Recognition †

• Parag Singla and Raymond J. Mooney. Abductive Markov Logic for Plan Recognition. AAAI2011. pp 1069-1075. pdf
• Nate Blaylock and James Allen. Hierarchical Instantiated Goal Recognition. MOO2006. pdf
• Nate Blaylock and James Allen. Fast Hierarchical Goal Schema Recognition. AAAI2006. pdf
• Douglas E. Appelt and Martha E. Pollack. Weighted Abduction for Plan Ascription. Technical Note 491, SRI International, 1992. pdf
• Sandra Carberry. Techniques for Plan Recognition. User Modeling and User-Adapted Interaction, 11(1-2), pp. 31-48, 2001. pdf

Corpus †

• Penn Discourse Treebank
• RST Discourse Treebank
• Discourse Graphbank: paper LDC
• SDRT annotations of dialogues: DISCOR project
• 名詞間関係コーパス

Knowledge Acquisition †

• Doo Soon Kim and Bruce Poter. Integrating declarative knowledge : Issues, Algorithms and Future Work. AAAI2008. pdf
• Jonathan Berant, Tel Aviv and Jacob Goldberger. Global Learning of Typed Entailment Rules. ACL2011. (to appear) pdf
• Stefan Schoenmackers, Jesse Davis, Oren Etzioni and Daniel Weld. Learning First-Order Horn Clauses from Web Text. EMNLP2010. pdf
• Nathanael Chambers and Dan Jurafsky. Unsupervised Learning of Narrative Schemas and their Participants. ACL2010. pdf

Lectures †

• University of Southern California: Introduction to NLP, Empirical Methods in Natural Language Processing
• MIT: Computational Models of Discourse
• Tohoku University: http://www.is.tohoku.ac.jp/_eng/introduction/laboratories/sis/iis_cs.html
• Story Understanding Resources
• David Poole and Alan Mackworth. Artificial Intelligence: Foundations of Computational Agents

Tools †

• WOFMC
• Stanford CoreNLP
• Mini-TACITUS
• PRISM
• enju
• C&C tools

Misc. †

• University of Texas at Austin Machine Learning Group: Publications
• Linguistic Problems and Complexities