Semantic Classes and Syntactic Ambiguity

SEMANTIC CLASSES AND SYNTACTIC AMBIGUITY Philip Resnik* Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104 resnik @ linc.cis.upenn.edu ABSTRACT In this paper we propose to define selectional preference and se- mantic similarity as information-theoretic relationships involving conceptual classes, and we demonstrate the applicability of these definitions to the resolution of syntactic ambiguity. The space of classes is defined using WordNet [8], and conceptual relationships are determined by means of statistical analysis using parsed text in the Penn Treebank. 1. INTRODUCTION The problem of syntactic ambiguity is a pervasive one. As Church and Patil [2] point out, the class of"every way ambigu- ous" constructions -- those for which the number of analyses is the number of binary trees over the terminal elements includes such frequent constructions as prepositional phrases, coordination, and nominal compounds. They suggest that until it has more useful constraints for resolving ambiguities, a parser can do little better than to efficiently record all the possible attachments and move on. In general, it may be that such constraints can only be supplied by analysis of the context, domain-dependent knowledge, or other complex inferential processes. However, we will sug- gest that in many cases, syntactic ambiguity can be resolved with the help of an extremely limited form of semantic knowl- edge, closely tied to the lexical items in the sentence. We focus on two relationships: selectional preference and se- mantic similarity. From one perspective, the proposals here can be viewed as an attempt to provide new formalizations for familiar but seldom carefully defined linguistic notions; elsewhere we demonstrate the utility of this approach in lin- guistic explanation [11]. From another perspective, the work reported here can be viewed as an attempt to generalize statisti- cal natural language techniques based on lexical associations, using knowledge-based rather than distributionally derived word classes. * This research has been supported by an IBM graduate fellowship and by DARPA grant N00014-90-J-1863. The comments of Eric Bnll, Marti Hearst, Jarnie Henderson, Aravind Joshi, Mark Liberman, Mitch Marcus, Michael Niv, and David Yarowsky are gratefully acknowledged. 2. CLASS-BASED STATISTICS A number of researchers have explored using lexical co- occurrences in text corpora to induce word classes [ 1,5, 9, 12], with results that are generally evaluated by inspecting the se- mantic cohesiveness of the distributional classes that result. In this work, we are investigating the alternative of using Word- Net, an explicitly semantic, broad coverage lexical database, to define the space of semantic classes. Although Word- Net is subject to the attendant disadvantages of any hand- constructed knowledge base, we have found that it provides an acceptable foundation upon which to build corpus-based techniques [10]. This affords us a clear distinction between domain-independent and corpus-specific sources of informa- tion, and a well-understood taxonomic representation for the domain-independent knowledge. Although WordNet includes data for several parts of speech, and encodes numerous semantic relationships (meronymy, antonymy, verb entailment, etc.), in this work we use only the noun taxonomy -- specifically, the mapping from words to …

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