Automatic Identification of motion verbs in WordNet and FrameNet

This paper discusses the automatic identification of motion verbs. The context is the recovery of unrealized location roles from discourse context or “locational inference”, a special case of missing argument recovery. We first report on a small corpus study on verb classes for which location roles are particularly relevant. This includes motion, orientation and position verbs. Then, we discuss the automatic recognition of these verbs on the basis of WordNet and FrameNet. For FrameNet, we obtain results up to 67% F-Score.

[1]  Sanda M. Harabagiu,et al.  Satisfying information needs with multi-document summaries , 2007, Inf. Process. Manag..

[2]  Stefan Thater,et al.  Assessing the impact of frame semantics on textual entailment , 2009, Natural Language Engineering.

[3]  Sara Tonelli,et al.  New Features for FrameNet - WordNet Mapping , 2009, CoNLL.

[4]  Barbara B. Levin,et al.  English verb classes and alternations , 1993 .

[5]  Joyce Yue Chai,et al.  Beyond NomBank: A Study of Implicit Arguments for Nominal Predicates , 2010, ACL.

[6]  Ido Dagan,et al.  Investigating a Generic Paraphrase-Based Approach for Relation Extraction , 2006, EACL.

[7]  Blake Stephen Howald,et al.  Linguistic spatial classifications of event domains in narratives of crime , 2010, J. Spatial Inf. Sci..

[8]  John B. Lowe,et al.  The Berkeley FrameNet Project , 1998, ACL.

[9]  Charles J. Fillmore,et al.  Frames and the semantics of understanding , 1985 .

[10]  Carina Silberer,et al.  Casting Implicit Role Linking as an Anaphora Resolution Task , 2012, *SEMEVAL.

[11]  Julie Weeds,et al.  Finding Predominant Word Senses in Untagged Text , 2004, ACL.

[12]  Christoph Müller,et al.  Multi-level annotation of linguistic data with MMAX 2 , 2006 .

[13]  David Herman,et al.  Spatial reference in narrative domains , 2001 .

[14]  Eneko Agirre,et al.  Personalizing PageRank for Word Sense Disambiguation , 2009, EACL.

[15]  Daniel Jurafsky,et al.  Automatic Labeling of Semantic Roles , 2002, CL.

[16]  Peter Norvig,et al.  Inference in Text Understanding , 1987, AAAI.

[17]  Charles J. Fillmore,et al.  Pragmatically Controlled Zero Anaphora , 1986 .

[18]  Hwee Tou Ng,et al.  A Machine Learning Approach to Coreference Resolution of Noun Phrases , 2001, CL.

[19]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[20]  Ido Dagan,et al.  Assessing the Role of Discourse References in Entailment Inference , 2010, ACL.

[21]  Jochen L. Leidner,et al.  Grounding spatial named entities for information extraction and question answering , 2003, HLT-NAACL 2003.

[22]  P. Resnik.  Selectional constraints: an information-theoretic model and its computational realization , 1996, Cognition.

[23]  G. Lakoff,et al.  Metaphors We Live by , 1981 .

[24]  M. Felisa Verdejo,et al.  Testing the Reasoning for Question Answering Validation , 2008, J. Log. Comput..

[25]  Siobhan Chapman.  Logic and Conversation , 2005 .

[26]  Ido Dagan,et al.  Recognizing textual entailment: Rational, evaluation and approaches , 2009, Natural Language Engineering.

[27]  Lei Shi,et al.  Putting Pieces Together: Combining FrameNet, VerbNet and WordNet for Robust Semantic Parsing , 2005, CICLing.

[28]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[29]  Christiane Fellbaum,et al.  MASC: the Manually Annotated Sub-Corpus of American English , 2008, LREC.

[30]  Roser Morante,et al.  Linking Events and Their Participants in Discourse , 2010 .

[31]  김두식,et al.  English Verb Classes and Alternations , 2006 .