SIG Discourse【談話研究会 / 2014】

About SIG Discourse

Special Interest Group on Discourse (SIG Discourse) discusses interesting findings of past/state-of-the-art researches. We also report and discuss research progress of our member. Typically, we read one paper (30 minutes) and discuss research progress of one person (40 minutes) at a time. The schedule is listed below.

Date

Wednesday 4:30-6:00pm

Member

乾，岡﨑，Canasai，井之上，杉浦，山本，周，Paul，大野，小林

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

Schedule

• 4/8 (Wed)
  • Kick-off Meeting (杉浦)
  • タスク紹介 (杉浦，山本，周，Paul，稲田，大野，小林)

• 3/11 (Wed)
  • Practice talk (周) : 内部資料
  • Progress report (小林): 内部資料

• 3/4 (Wed)
  • Paper reading (井之上)
    • Ashutosh Modi and Ivan Titov. Inducing Neural Models of Script Knowledge. Conference on Computational Natural Language Learning (CoNLL), 2014. 内部資料
  • Paper reading (大野)
    • Inducing Neural Models of Script Knowledge. Ashutosh Modi and Ivan Titov. paper, slide

• 2/25 (Wed)
  • Paper reading (稲田)
    • Chikara Hashimoto; Kentaro Torisawa; Julien Kloetzer; Motoki Sano; István Varga; Jong-Hoon Oh; Yutaka Kidawara, “Toward Future Scenario Generation: Extracting Event Causality Exploiting Semantic Relation, Context, and Association Features”, ACL 2014, paper, note, errata, 内部資料
  • Progress report (杉浦)

• 2/18 (Wed)
  • Paper reading (周)
    • Dojchinovski, Milan, and Tomáš Kliegr. “Entityclassifier. eu: real-time classification of entities in text with Wikipedia.” Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2013. 654-658.
    • paper1,paper2,slides
  • Progress report (山本): 仮説推論の高速化とWSCへの適用 (内部資料)

• 2/4 (Wed)
  • Progress report (大野): 内部資料
  • Paper reading (Paul) Joonsuk Park and Claire Cardie. 2014. Identifying Appropriate Support for Propositions in Online User Comments Paper Slides

• 1/28 (Wed)
  • Progress report (Canasai): slides
  • Paper reading（山本）
    • Marek Rei and Ted Briscoe, 2014, Looking for Hyponyms in Vector Space, Proceedings of the Eighteenth Conference on Computational Natural Language Learning, pp.68-77
    • slide, paper

• 1/14 (Wed)
  • Paper reading（杉浦）
    • +/-EffectWordNet: Sense-level Lexicon Acquisition for Opinion Inference, Yoonjung Choi and Janyce Wiebe, EMNLP2014
    • paper, poster

• 1/7 (Wed)
  • Progress report（Paul） 内部資料
  • Paper reading（井之上）
    • Wang et al. 2012. Exact Soft Confidence-Weighted Learning. ICML2012.
    • 内部資料

• 12/17 (Wed)
  • Progress report（小林）: 内部資料
  • Paper reading（大野）
    • Reasoning With Neural Tensor Networks for Knowledge Base Completion, Richard Socher+, NIPS2013 paper, 内部資料

• 12/10 (Wed)
  • Practice talk（山本）: 内部資料

• 12/3 (Wed)
  • Progress report（稲田）
    • 時間関係の付与対象同定へ向けた event & state アノテーション 内部資料
  • Paper reading（佐藤雅）
    • Transition-based Semantic Role LabelingUsing Predicate Argument Clustering, Jinho D. Choi and Martha Palmer, ACL 2011

slides

• 11/26 (Wed)
  • Progress report（周）
    • 内部資料

• Paper reading（Canasai）
  • Tackling Winograd Schemas by Formalizing Relevance Theory in Knowledge Graphs, Peter Schüller, KR2014 paper slides
• 11/19 (Wed)
  • Progress report（杉浦）
    • 内部資料
  • Paper reading（小林）
    • Jonathan Berant & Vivek Srikumar et al. : Modeling Biological Processes for Reading Comprehension, EMNLP2014

slide

• Supplement, Project page, Data set, Video

• 11/12 (Wed)
  • Progress report（井之上）
  • Paper reading（周）
    • A Rule-Based System for Unrestricted Bridging Resolution:Recognizing Bridging Anaphora and Finding Links to Antecedents paper marked paper slides

• 11/5 (Wed)
  • Paper reading（山本）
    • Extracting Lexically Divergent Paraphrases from Twitter paper slide

• 10/29 (Wed)
  • Progress report（大野）
    • 内部資料
  • Paper reading（杉浦）
    • Luciano Del Corro, Rainer Gemulla and Gerhard Weikum. Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning, EMNLP2014. pdf 内部資料

• 10/22 (Wed)
  • Progress report（Canasai）
    • An Example-Based Approach to Difficult Pronoun Resolution. note
  • Paper reading（井之上）
    • William Yang Wang, Kathryn Mazaitis and William W. Cohen. Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic. CIKM2013. pdf,
    • Matt Gardner, Partha Talukdar, Jayant Krishnamurthy and Tom Mitchell. Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases. EMNLP2014. pdf, or
    • Gabor Angeli and Christopher D. Manning. NaturalLI: Natural Logic Inference for Common Sense Reasoning. EMNLP2014. pdf 内部資料

• 10/15 (Wed)
  • Paper reading（周）
    • Charton, Eric, et al. “Improving Entity Linking using Surface Form Refinement.” LREC 2014.
    • paper slide

• 10/8 (Wed)
  • Progress report（小林）

• 9/24 (Wed) 16:30- G10
  • Progress report (井之上)
    • 内部資料
  • Paper reading (佐藤)
    • Predicate argument structure analysis using partially annotated corpora (Yoshino+, IJCNLP, 2013)
    • slide paper

• 9/17 (Wed) 16:30- G10
  • Progress report (大野)
    • 内部資料

• 9/3 (Wed) 16:30- G9
  • Progress report (大野)
  • Paper reading (山本) : Is This a Paraphrase? What Kind? Paraphrase Boundaries and Typology, Marta Vila, M. Antònia Martí, Horacio Rodríguez (slide, paper)

• 7/23 (Wed) 16:30- G8
  • Progress report (佐藤)
    • 内部資料
  • Paper reading (杉浦)
    • Qi Zhang, Jin Qian, Huan Chen, Jihua Kang, Xuanjing Huang. Discourse Level Explanatory Relation Extraction from Product Reviews Using First-order Logic. ACL2013.
    • paper slides paper(マーク付き)

• 7/16 (Wed) 16:30- G7
  • Progress report (稲田)
    • 内部資料
  • Paper reading (井之上)
    • Carlos R.C. Souza and Paulo E. Santos. Probabilistic Logic Reasoning about Traffic Scenes. TAROS 2011.
    • paper slides

• 7/9 (Wed) 16:30- G6
  • Progress report (周)
    • 内部資料
  • Paper reading (Canasai)
    • Learning-based Multi-Sieve Co-reference

Resolution with Knowledge (Lev Ratinov and Dan Roth, EMNLP-2012)

• paper slides

• 7/2 (Wed) 16:30- G5
  • Practice talk for pre-master’s thesis presentation (Paul)
    • 内部資料
  • Paper reading (小林)
    • A Generative Model for User Simulation in a Spatial Navigation Domain (Eshky et al., EACL, 2014)
    • paper
    • slide

• 6/25 (Wed) 16:30- G4
  • Practice talk for SIGNL-217 (山本) : ( 内部資料)
  • Paper reading (大野)
    • A Latent Dirichlet Allocation method for Selectional Preference (Alan et al., ACL, 2010)
    • paper
    • slide

• 6/18 (Wed) 16:30- G3
  • Progress report (杉浦・井之上) (内部資料)
  • Paper reading (佐藤)
    • Recurrent Convolutional Neural Networks for Discourse Compositionality (Kalchbrenner et al., CVSC, 2013)
    • Slides
    • Paper

• 6/11 (Wed) 16:30- G2
  • Progress report (Canasai) slides
  • Paper reading (稲田)
    • Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise (J Whitehill, et al., NIPS, 2009)
    • slides
    • paper
    • Supplementary Materials

• 6/4 (Wed) 16:30- G1
  • Paper reading (Paul)
    • Identifying Controversial Issues and their Sub-topics in News Articles (Yoonjung Choi, Yuchul Jung, Sung-Hyon Myaeng – 2010)
    • Slides
    • Paper

• 5/28 (Wed) 16:30- G10
  • Progress report (大野)
    • 選択選好とフレーム検索
    • 内部資料
  • Paper reading (周)
    • Jesse Dunietz and Dan Gillick. A New Entity Salience Task with Millions of Training Examples. EACL 2014
    • Paper
    • Slide

• 5/21 (Wed) 16:30- G9
  • Paper reading (山本): Scaling Inference for Markov Logic via Dual Decomposition (論文, 内部資料)

• 5/14 (Wed) 16:30- G8
  • Progress report (稲田)
    • 時間関係認識に基づく大規模な事象間関係知識の構築へ向けて
    • 内部資料
    • 補足資料１
  • Paper reading (杉浦)
    • Attapol Rutherford and Nianwen Xue. Discovering Implicit Discourse Relations Through Brown Cluster Pair Representation and Coreference Patterns. EACL2014
    • 内部資料
    • 論文

• 5/7 (Wed) 16:30- G7
  • Progress report (佐藤)
    • 就活報告＋α
  • Paper reading (Canasai)
    • Sarcasm as Contrast between a Positive Sentiment and Negative Situation, Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, Ruihong Huang, EMNLP-2013
    • paper slides

• 4/30 (Wed) 16:30- G6
  • Paper reading (井之上)
    • Somdeb Sarkhel, Deepak Venugopal, Parag Singla and Vibhav Gogate. Lifted MAP Inference for Markov Logic Networks. AISTATS2014.
    • 資料
    • 論文: アノテーション付き 原論文

• 4/23 (Wed) 16:30- G5
  • Progress report (周) 内部資料
  • Paper reading (小林)
    • Generating Natural-Language Video Descriptions Using Text-Mined Knowledge, Niveda Krishnamoorthy & Girish Malkarnenkar & Raymond J. Mooney & Kate Saenko & Sergio Guadarrama, AAAI, 2013
    • 論文, 内部資料

• 4/16 (Wed) 16:30-
  • Paper reading (山本)
    • Online Inference-Rule Learning from Natural-Language Extractions, S. Raghavan & R. J. Mooney, AAAI, 2013.
    • 論文, 内部資料

• 4/10 (Thu) 15:00-
  • Kick-off Meeting (井之上、杉浦、山本、周)
    • 井之上: Study of Discourse is Fun (内部資料)
    • 杉浦: タスク紹介 ー共参照解析と談話関係認識ー (内部資料)
    • 山本: タスク紹介 ー 談話解析のための論理推論機構の構築 (内部資料)
    • 周: タスク紹介 ー Entity Linking for Knowledge Base Population (内部資料)

• 4/3 (Thu)
  • Progress report (Canasai)
  • Progress report (井之上)
  • Progress report (杉浦)

• Spring 2013-Winter 2014
• Spring 2012-Winter 2013
• Spring 2011-Winter 2012
• Spring 2010-Winter 2011

Assignment

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

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