Explaining away ambiguity: Learning verb selectional preference with Bayesian networks

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dc.creatorCiaramita, Massimiliano
dc.creatorJohnson, Mark
dc.date2000-08-22
dc.date.accessioned2026-07-07T03:16:28Z
dc.date.available2026-07-07T03:16:28Z
dc.descriptionThis 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.
dc.identifierhttps://arxiv.org/abs/cs/0008020
dc.identifierhttp://arxiv.org/abs/cs/0008020
dc.identifierProceedings of the 18th International Conference on Computational Linguistics, Saarbrucken, Germany, Vol.1, 2000, p.187
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/30368
dc.subjectComputation and Language
dc.subjectArtificial Intelligence
dc.subjectI.2.7
dc.titleExplaining away ambiguity: Learning verb selectional preference with Bayesian networks
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