Lexical Clustering and Definite Description Interpretation

Massimo Poesio, Sabine Schulte im Walde, and Chris Brew

We present preliminary results concerning the use of lexical clustering algorithms to acquire the kind of lexical knowledge needed to resolve definite descriptions, and in particular what we call 'inferential' descriptions. We tested the hypothesis that the antecedent of an inferential description is primarily identified on the basis of its semantic distance from the description; we also tested several variants of the clustering algorithm. We found that the choice of parameters has a clear effect, and that the best results are obtained by measuring the distance between lexical vectors using the cosine measure. We also found, however, that factors other than semantic distance play the main role in the majority of cases; but in those cases in which the sort of lexical knowledge we acquired is the main factor, the algorithms we used performed reasonably well; several standing problems are discussed.

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