Recent Developments in Computational Semantics (SS 2012)

Reading

General Background Reading

• D. Jurafsky, J. Martin (2009): Speech and Language Processing. Pearson Education. Chapter 20: Computational Lexical Semantics pp 671-714.
• A. Koller, M. Pinkal (to appear): Semantic Research in Computational Linguistics. In: Semantics: An International Handbook of Natural Language Meaning. de Gruyter (PDF)

Literature for seminar topics

• R. Bhagat, P. Pantel and E. Hovy (2007): LEDIR An Unsupervised Algorithm for Learning Directionality of Inference Rules. Proceedings of EMNLP-CoNLL 2007 (PDF)
• E. Bruni, G. Tran, M. Baroni(2011): Distributional semantics from text and images. Proceedings of GEMS Workshop, EMNLP 2011 (PDF)
• N. Chambers, D. Jurafsky (2008): Unsupervised learning of narrative event chains. Proc. of ACL 2008 (PDF)
• N. Chambers, D. Jurafsky (2009): Unsupervised Learning of Narrative Schemas and their Participants. Proc. of ACL 2009 (PDF)
• G. Dinu, M. Lapata (2010): Measuring Distributional Similarity in Context. Proceedings of EMNLP-10. (PDF)
• G. Dinu (2011): Word meaning in context: A probabilistic model and its application to Question Answering. Doctoral Dissertation. Universität des Saarlandes (PDF)
• K. Erk, S. Pado (2008): A structured vector space model for word meaning in context. Proceedings of EMNLP 2008. (PDF)
• K. Erk, S. Pado (2010): Exemplar-Based Models for Word Meaning in Context. Proceedings of ACL 2010. (PDF)
• Y. Feng; M. Lapata (2010): Visual Information in Semantic Representation. Proceedings of NAACL(PDF)
• M. Geffet, I. Dagan (2005): The Distribuational Inclusion Hypotheses and Lexical Entailment. Proceedings of ACL 2005. (PDF)
• D. Gildea, D. Jurafsky (2002): Automatic Labeling of Semantic Roles. In Computational Linguistics 2002. (PDF)
• J. Lang, M. Lapata (2011): Unsupervised Semantic Role Induction with Graph Partitioning. Proceedings of EMNLP(PDF)
• J. Lang, M. Lapata (2011a): Unsupervised Semantic Role Induction via Split-Merge Clustering. Proceedings of ACL(PDF)
• L. Li, B. Roth, C. Sporleder (2010): Topic Models for Word Sense Disambiguation and Token-Based Idiom Detection. Proceedings of ACL 2010. (PDF)
• D. Lin, P. Pantel (2001): DIRT - Discovery of Inference Rules from Text. Proceedings of ACM Conference on Knowledge Discovery and Data Mining (KDD-01). (PDF)
• D. McCarthy (2009): Word Sense Disambiguation: An Overview. In: Language and Linguistics Compass 3 (2) pp 537-558. (PDF)
• R. Mihalcea (2007): Using Wikipedia for Automatic Word Sense Disambiguation. Proceedings of NAACL. (PDF)
• J. Mitchell, M. Lapata (2008): Vector-based models of semantic composition. Proceedings of ACL 2008. (PDF)
• M. Palmer, D. Gildea, P. Kingsbury (2005): The Proposition Bank: An Annotated Corpus of Semantic Roles. Computational Linguistics 31 ( PDF)
• S. Pradhan, W. Ward, J. Martin (2008): Towards Robust Semantic Role Labeling. Computational Linguistics 34. (PDF)
• P. Pantel, R. Bhagat, B. Coppola, T. Chklovski, E. Hovy (2007): ISP: Learning Inferential Selectional Preferences, Proceedings of NAACL/HLT 2007. (PDF)
• S. Ponzetto, R. Navigli (2010): Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems. Proceedings of ACL 2010. (PDF)
• M. Regneri, A. Koller, M. Pinkal (2009): Learning Script Knowledge with Web Experiments. Proceedings of ACL 2010. (PDF)
• M. Regneri, A. Koller, J. Ruppenhofer, M. Pinkal (2009): Learning script participants from unlabeled data. Proceedings of RANLP (PDF)
• J. Reisinger, R. Mooney (2010): Multi-Prototype Vector-Space Models of Word Meaning. Proc. of NAACL 2010 (PDF)
• I. Szpektor, H. Tanev, I. Dagan, B Coppola (2004): Bonaventura: Scaling Web-based Acquisition of Entailment Relations. Proceedings of EMNLP 2004. (PDF)
• S. Thater, H. Fürstenau, M. Pinkal (2010): Contextualizing Semantic Representations Using Syntactically Enriched Vector Models. Proceedings of ACL 2010. (PDF)
• S. Thater, H. Fürstenau, M. Pinkal (2011): Word Meaning in Context: A Simple and Effective Vector Model. Proceedings of IJCNLP 2011 (PDF)
• I. Titov; A. Klementiev A Bayesian Model for Unsupervised Semantic Parsing. Proc. of ACL (PDF)
• P. D. Turney and P. Pantel (2010) "From Frequency to Meaning: Vector Space Models of Semantics", JAIR Volume 37, pages 141-188 (PDF)
• D. Yarowsky (1995): Unsupervised word-sense disambiguation rivaling suopervised methods. Proceedings of ACL 1995 (PDF)

Further Literature

• J. van Benthem (2007): A brief history of natural logic. ILLC research report 2007 (PDF)
• A. Burchardt, M. Pennacchiotti, S. Thater, M. Pinkal (2009): Assessing the impact of frame semantics on textual entailment. In: Journal of Natural Language Engineering, Special issue on Textual Entailment.
• T. Chklovski, P. Pantel (2004): VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations. Proc. of EMNLP-04 (PDF)
• I. Dagan, O. Glickman, B. Magnini (2006): The PASCAL Recognising Textual Entailment Challenge. In J. Quiñonero-Candela, I. Dagan, B. Magnini, F. d'Alcheé-Buc: Machine Learning Challenges. Lecture Notes in Computer Science, Springer. (PDF)
• K. Erk(2007): A Simple, Similarity-based Model for Selectional Preferences. Proceedings of ACL 2007. (PDF)
• K. Erk, D. McCarthy, N. Gaylord (2009): Investigations on Word Senses and Word Usages. Proceedings of ACL 2009. (PDF)
• K. Erk, D. McCarthy (2009): Graded word sense assignment. Proceedings of EMNLP 2009. (PDF)
• E. Grefenstette , M. Sadrzadeh, S. Clark, B. Coecke , S.Pulman (2011): Concrete Sentence Spaces for Compositional Distributional Models of Meaning. Proceedings of IWCS 2011. (PDF)
• D. Lin (1998): Automatic Retrieval and Clustering of Similar Words. Proc. of COLING-ACL98 (PDF)
• B. MacCartney (2009): Natural Language Inference. PhD Dissertation Stanford. (PDF)
• B. MacCartney, C.D. Manning (2007): Natural logic for textual inference. ACL Workshop on Textual Entailment and Paraphrasing 2007. (PDF)
• C. Manning (2006): Local Textual Inference: It's hard to circumscribe, but you know it when you see it - and NLP needs it. ms. (PDF)
• D. McCarthy, J. Carroll (2003): Disambiguating nouns, verbs and adjectives using automatically acquired selectional preferences. Computational Linguistics, 29(4). pp 639-654. (PDF)
• R. Mihalcea, D. Moldovan (1999): A Method for Word Sense Disambiguation of Unrestricted Text. Proc. of ACL 99
• Nairn, Condoravdi, Karttunen (2006): Computing relative polarity for textual inference. ICoS-5, Workshop Proceedings. (PDF)
• V. Punyakanok, D. Roth, W. Yih (2008): The Importance of Syntactic Parsing and Inference in Semantic Role Labeling. Computational Linguistics 2008. (PDF)
• Schütze, Hinrich. (1998): Automatic Word Sense Discrimination. Computational Linguistics 24. S.97-123 ( PDF)
• J. Weeds, D. Weir (2005): Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity. Computational Linguistics 2005 (PDF)
• A. Zaenen, L. Karttunen, R. Crouch (2005): Local Textual Inference: can it be defined or circumscribed?. Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment, 2005. (PDF)