Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjectivenoun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance.

[1]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[2]  David Yarowsky,et al.  A method for disambiguating word senses in a large corpus , 1992, Comput. Humanit..

[3]  Ted Briscoe,et al.  Generalized Probabilistic LR Parsing of Natural Language (Corpora) with Unification-Based Grammars , 1993, CL.

[4]  George A. Miller,et al.  A Semantic Concordance , 1993, HLT.

[5]  David Elworthy,et al.  Does Baum-Welch Re-estimation Help Taggers? , 1994, ANLP.

[6]  Ted Briscoe,et al.  Developing and Evaluating a Probabilistic LR Parser of Part-of-Speech and Punctuation Labels , 1995, IWPT.

[7]  Francesc Ribas,et al.  On Learning more Appropriate Selectional Restrictions , 1995, EACL.

[8]  Hwee Tou Ng,et al.  Integrating Multiple Knowledge Sources to Disambiguate Word Sense: An Exemplar-Based Approach , 1996, ACL.

[9]  Diana McCarthy Word Sense Disambiguation for Acquisition of Selectional Preferences , 1997 .

[10]  Philip Resnik,et al.  Selectional Preference and Sense Disambiguation , 1997 .

[11]  Ted Briscoe,et al.  Parser evaluation: a survey and a new proposal , 1998, LREC.

[12]  Marc Light,et al.  Hiding a Semantic Class Hierarchy in a Markov Model , 1998 .

[13]  Robert Krovetz,et al.  More than One Sense Per Discourse , 1998 .

[14]  Hang Li,et al.  Generalizing Case Frames Using a Thesaurus and the MDL Principle , 1995, CL.

[15]  Vito Pirrelli,et al.  SENSE: an analogy-based Word Sense Disambiguation system , 1999, Nat. Lang. Eng..

[16]  Ted Briscoe,et al.  Corpus Annotation for Parser Evaluation , 1999, ArXiv.

[17]  Siobhan Devlin,et al.  Simplifying Text for Language-Impaired Readers , 1999, EACL.

[18]  Massimiliano Ciaramita,et al.  Explaining away ambiguity: Learning verb selectional preference with Bayesian networks , 2000, COLING.

[19]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[20]  Eneko Agirre,et al.  Learning class-to-class selectional preferences , 2001, CoNLL.

[21]  Diana McCarthy,et al.  Lexical acquisition at the syntax-semantics interface : diathesis alternations, subcategorization frames and selectional preferences , 2001 .

[22]  John A. Carroll,et al.  Applied morphological processing of English , 2001, Natural Language Engineering.

[23]  Yorick Wilks,et al.  The Interaction of Knowledge Sources in Word Sense Disambiguation , 2001, CL.

[24]  Anna Korhonen,et al.  Improving Subcategorization Acquisition with WSD , 2002, SENSEVAL.

[25]  Marc Light,et al.  Statistical models for the induction and use of selectional preferences , 2002, Cogn. Sci..

[26]  H. Dang,et al.  Making fine-grained and coarse-grained sense distinctions, both manually and automatically , 2006, Natural Language Engineering.