ABSTRACT
Knowledge of which words are able to fill particular argument slots of a predicate can be used for structural disambiguation. This paper describes a proposal for acquiring such knowledge, and in line with much of the recent work in this area, a probabilistic approach is taken. We develop a novel way of using a semantic hierarchy to estimate the probabilities, and demonstrate the general approach using a prepositional phrase attachment experiment.
- Eric Brill and Philip Resnik. 1994. A rule-based approach to prepositional phrase attachment disambiguation. In Proceedings of the fifteenth International Conference on Computational Linguistics. Google ScholarDigital Library
- Eugene Charniak. 1993. Statistical Language Learning. The MIT Press. Google ScholarDigital Library
- Stephen Clark and David Weir. 1999. An iterative approach to estimating frequencies over a semantic hierarchy. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pages 258--265.Google Scholar
- Michael Collius. 1995. Prepositional phrase attachment through a backed-off model. In Proceedings of the Third Workshop on Very Large Corpora, pages 27--38, Cambridge, Massachusetts.Google Scholar
- Michael Collins. 1996. A new statistical parser based on bigram lexical dependencies. In Proceedings of the 34th Annual Meeting of the ACL, pages 184--191. Google ScholarDigital Library
- Michael Collins. 1997. Three generative, lexicalised models for statistical parsing. In Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pages 16--23. Google ScholarDigital Library
- Ted Dunning. 1993. Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1):61--74. Google ScholarDigital Library
- Christiane Fellbaum, editor. 1998. WordNet An Electronic Lexical Database. The MIT Press.Google Scholar
- Donald Hindle and Mats Rooth. 1993. Structural ambiguity and lexical relations. Computational Linguistics, 19(1):103--120. Google ScholarDigital Library
- David Howell. 1997. Statistical Methods for Psychology: 4th ed. Duxbury Press.Google Scholar
- Hang Li and Naoki Abe. 1998. Generalizing case frames using a thesaurus and the MDL principle. Computational Linguistics, 24(2):217--244. Google ScholarDigital Library
- Adwait Ratnaparkhi, Jeff Reynar, and Salim Roukos. 1994. A maximum cntropy model for prepositional phrase attachment. In Proceedings of the ARPA Human Language Technology Workshop, pages 250--255. Google ScholarDigital Library
- Adwait Ratnaparkhi. 1998. Unsupervised statistical models for prepositional phrase attachment. In Proceedings of the Seventeenth International Conference on Computational Linguistics, Montreal, Canada, Aug. Google ScholarDigital Library
- Philip Resnik. 1993. Selection and Information: A Class-Based Approach to Lexical Relationships. Ph. D. thesis, University of Pennsylvania. Google ScholarDigital Library
- Francesc Ribas. 1995. On learning more appropriate selectional restrictions. In Proceedings of the Seventh Conference of the European Chapter of the Association for Computational Linguistics, Dublin, Ireland. Google ScholarDigital Library
- Jiri Stetina and Makoto Nagao. 1997. Corpus based PP attachment ambiguity resolution with a semantic dictionary. In Proceedings of the Fifth Workshop on Very Large Corpora, pages 66--80, Beijing and Hong Kong.Google Scholar
- David Yarowsky. 1992. Word-sense disambiguation using statistical models of Roget's categories trained on large corpora. In Proceedings of COLING-92, pages 454--460. Google ScholarDigital Library
- Jakub Zavrel and Walter Daelemans. 1997. Memory-based learning: Using similarity for smoothing. In Proceeding of ACL/EACL-97, Madrid, Spain. Google ScholarDigital Library
- A class-based probabilistic approach to structural disambiguation
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