  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.
