Explaining away ambiguity: learning verb selectional preference with Bayesian networks

Massimiliano Ciaramita, Mark Johnson

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This paper presents a Bayesian model for unsupervised learning of verb selectional preferences. For each verb the model creates a Bayesian network whose architecture is determined by
the lexical hierarchy of Wordnet and whose parameters are estimated from a list of verb-object pairs found from a corpus. "Explaining away", a well-known property of Bayesian networks, helps the model deal in a natural fashion with word sense ambiguity in the training data. On a word sense disambiguation test our model performed better than other state of the art systems for unsupervised learning of selectional preferences. Computational complexity problems, ways of improving this approach and methods for implementing "explaining away" in other graphical frameworks are discussed.
Original languageEnglish
Title of host publicationProceedings of the 18th International Conference on Computational Linguistics
Subtitle of host publicationCOLINGS 2000
Place of PublicationNew Brunswick, NJ
PublisherAssociation for Computational Linguistics
Number of pages7
ISBN (Print)1-55860-717-X
Publication statusPublished - 2000
Externally publishedYes
EventInternational Conference on Computational Linguistics (18th : 2000) - Universität des Saarlandes, Saarbrücken, Germany
Duration: 31 Jul 20004 Aug 2000


ConferenceInternational Conference on Computational Linguistics (18th : 2000)
Abbreviated titleCOLING 2000

Bibliographical note

Copyright the Publisher 2000. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


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