One Sense per Collocation

ONE SENSE PER COLLOCATION David Yarowsky* Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 yarowsky@unagi.cis.upenn.edu ABSTRACT Previous work [Gale, Church and Yarowsky, 1992] showed that with high probability a polysemous word has one sense per discourse. In this paper we show that for certain definitions of collocation, a polysemous word exhibits essentially only one sense per collocation. We test this empirical hypothesis for several definitions of sense and collocation, and discover that it holds with 90-99% accuracy for binary ambiguities. We utilize this property in a disambiguation algorithm that achieves precision of 92% using combined models of very local context. 1. INTRODUCTION The use of collocations to resolve lexical ambiguities is cer- tainly not a new idea. The first approaches to sense dis- ambiguation, such as [Kelly and Stone 1975], were based on simple hand-built decision tables consisting almost ex- clusively of questions about observed word associations in specific positions. Later work from the AI community relied heavily upon selectional restrictions for verbs, although pri- marily in terms of features exhibited by their arguments (such as +DRINKABLE) rather than in terms of individual words or word classes. More recent work [Brown et al. 1991][Hearst 1991] has utilized a set of discrete local questions (such as word-to-the-right) in the development of statistical decision procedures. However, a strong trend in recent years is to treat a reasonably wide context window as an unordered bag of in- dependent evidence points. This technique from information retrieval has been used in neural networks, Bayesian discrim- inators, and dictionary definition matching. In a comparative paper in this volume [Leacock et al. 1993], all three methods under investigation used words in wide context as a pool of evidence independent of relative position. It is perhaps not a coincidence that this work has focused almost exclusively on nouns, as will be shown in Section 6.2. In this study we will return again to extremely local sources of evidence, and show that models of discrete syntactic relationships have considerable advantages. *This research was supported by an NDSEG Fellowship and by DARPA grant N00014-90-J-1863. The author is also affiliated with the Linguistics Research Department of AT&T Bell Laboratories, and greatly appreciates the use of its resources in support of this work. He would also like to thank Eric Bfill, Bill Gale, Libby Levison, Mitch Marcus and Philip Resnik for their valuable feedback. 2. DEFINITIONS OF SENSE The traditional definition of word sense is "One of several meanings assigned to the same orthographic string". As meanings can always be partitioned into multiple refinements, senses are typically organized in a tree such as one finds in a dictionary. In the extreme case, one could continue making refinements until a word has a slightly different sense every time it is used. If so, the title of this paper is a tautology. However, the studies in this paper are focused on the sense distinctions at the top of the tree. A good working definition of the distinctions considered are those …

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