Semantically Significant Patterns in Dictionary Definitions

SEMANTICALLY SIGNIFICANT PATTERNS IN DICTIONARY DEFINITIONS * Judith Markowitz Computer Science Department De Paul University, Chicago, IL 60604 Thomas Ahlswede Marth~ Evens Computer Science Department Illinois Institute of Technology, Chicago, Ii 60616 ABSTRACT Natural language processing systems need large lexicons containing explicit information about lexical-semantlc relationships, selection restrictions, and verb categories. Because the labor involved in constructing such lexicons by hand is overwhelming, we have been trying to construct lexical entries automatically from information available in the machine-readable version of Webst@r's ~@ve~h Col!eglate Dictionary. This work is rich in implicit information; the problem is to make it explicit. This paper describes methods for finding taxonomy and set-membership relationships, recognizing nouns that ordinarily represent human beings, and identifying active and stative verbs and adjectives. INTRODUCTION Large natural language processing systems need lexicons much larger than those available today with explicit information about lexlcal-semantic re%ationships, about usage, about forms, about morphology, about case frames and selection restrictions and other kinds of collocational information. Apresyan, Mel'cuk, and Zholkovsky studied the kind of explicit lexical information needed by non-native speakers of a language. Their Explanatory-Combinatory Dictionary (1970) explains how each word is used and how it combines with others in phrases and sentences. Their dream has now been realized in a full-scale dictionary of Russian (Mel'cuk and Zholkovsky, 1985) and in example entries for French (Mel'cuk et al., 1984). Computer programs need still more explicit and detailed information. We have discussed elsewhere the kind of lexical information needed in a question answering system (Evens and Smith, 1978) and by a system to generate medical case reports (Li et al., 1985). This research was supported by the National Science Foundation under IST-85- 10069. A number of experiments have shown that relational thesauri can significantly improve the effectiveness of an information retrieval system (Fox, 1980; Evens et al., 1985; Wang et al., 1985). A relational thesaurus is used to add further terms to the lquery, terms that are related to the ~riglnal by lexlcal relations like synonymy, taxonomy, set-membership, or the part- whole relation, among others. The addition of these related terms enables the system to identify more relevant documents. The development of such relational thesauri would be comparatively simple if we had a large lexicon containing relational information. (A comparative study of lexical relations can be found in Evens et al., 1980). The work involved in developing a lexicon for a large subset of English is so overwhelming, that it seems appropriate to try to build a lexicon automatically by analyzing information in a machine-readable dictionary. A collegiate level dictionary contains an enormous amount of information about thousands of words in the natural language it describes. This information is presented in a form intended to be easily understood and used by a human being with at least some command of the language. Unfortunately, even when the dictionary has been transcribed into machine-readable form, the knowledge which a human user can acquire from the dictionary is not readily available to the computer. There have been a …

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