Integrating selectional preferences in WordNet
<|reference_start|>Integrating selectional preferences in WordNet: Selectional 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.<|reference_end|>
@article{agirre2002integrating,
    title={Integrating selectional preferences in WordNet},
    author={Eneko Agirre and David Martinez},
    journal={Proceedings of First International WordNet Conference. Mysore
  (India). 2002},
    year={2002},
    archivePrefix={arXiv},
    eprint={cs/0204027},
    primaryClass={cs.CL}
}