Estimating satisfactoriness of selectional restriction from corpus without a thesaurus

A selectional restriction specifies what combinations of words are semantically valid in a particular syntactic construction. This is one of the basic and important pieces of knowledge in natural language processing and has been used for syntactic and word sense disambiguation. In the case of acquiring the selectional restriction for many combinations of words from a corpus, it is necessary to estimate whether or not a word combination that is not observed in the corpus satisfies the selectional restriction. This paper proposes a new method for estimating the degree of satisfaction of the selectional restriction for a word combination from a tagged corpus, based on the multiple regression model. The independent variables of this model correspond to modifiers. Unlike a conventional multiple regression analysis, the independent variables are also parameters to be learned. We experiment on estimating the degree of satisfaction of the selectional restriction for Japanese word combinations (noun, postpositional-particle, verb). The experimental results indicate that our method estimates the degree of satisfaction of a word combination not very well observed in the corpus, and that the accuracy of syntactic disambiguation using the co-occurrencies estimated by our method is higher than using co-occurrence probabilities smoothed by previous methods.