Integrating selectional preferences in WordNet

dc.creatorAgirre, Eneko
dc.creatorMartinez, David
dc.date2002-04-11
dc.date.accessioned2026-07-07T03:18:18Z
dc.date.available2026-07-07T03:18:18Z
dc.descriptionSelectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class. This paper extends previous statistical models to class-to-class preferences, and presents a model that learns selectional preferences for classes of verbs, together with an algorithm to integrate the learned preferences in WordNet. The theoretical motivation is twofold: different senses of a verb may have different preferences, and classes of verbs may share preferences. On the practical side, class-to-class selectional preferences can be learned from untagged corpora (the same as word-to-class), they provide selectional preferences for less frequent word senses via inheritance, and more important, they allow for easy integration in WordNet. The model is trained on subject-verb and object-verb relationships extracted from a small corpus disambiguated with WordNet senses. Examples are provided illustrating that the theoretical motivations are well founded, and showing that the approach is feasible. Experimental results on a word sense disambiguation task are also provided.
dc.description9 pages
dc.identifierhttps://arxiv.org/abs/cs/0204027
dc.identifierhttp://arxiv.org/abs/cs/0204027
dc.identifierProceedings of First International WordNet Conference. Mysore (India). 2002
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/31057
dc.subjectComputation and Language
dc.subjectI.2.7
dc.titleIntegrating selectional preferences in WordNet
dc.typetext

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