Lexical Disambiguation using Simulated Annealing

Lexical Disambiguation using Simulated Annealing Jim Cowie, Joe Guthrie, Louise Guthrie Computing Research Laboratory Box 30001 New Mexico State University Las Cruces, NM 88003-0001 ABSTRACT The resolution of lexical ambiguity is important for most nat- ural language processing tasks, and a range of computational techniques have been proposed for its solution. None of these has yet proven effective on a large scale. In this paper, we de- scribe a method for lexical disambiguation of text using the definitions in a machine-readable dictionary together with the technique of simulated annealing. The method operates on complete sentences and attempts to select the optimal combinations of word senses for all the words in the sentence simultaneously. The words in the sentences may be any of the 28,000 headwords in Longman's Dictionary of Contempo- rary English (LDOCE) and are disambiguated relative to the senses given in LDOCE. Our initial results on a sample set of 50 sentences are comparable to those of other researchers, and the fully automatic method requires no hand coding of lexical entries, or hand tagging of text. 1. Introduction The problem of word-sense disambiguation is central to text processing. Recently, promising computational methods have been suggested [Lesk, 1987; McDonald et al., 1990; Wilks et al., 1990; Zernik and Jacobs, 1990; Guthrie et al., 1991; Hearst, 1991] which attempt to use the local context of the word to be disambiguated to- gether with information about each of its word senses to solve this problem. Lesk [1987] described a technique which measured the amount of overlap between a dictionary sense definition and the local context of the word to be disambiguated to successfully disambiguate the word "cone" in the phrases "pine cone" and "ice cream cone". Later researchers have extended this basic idea in various ways. Wilks et al., [1990] identified neighborhoods of the 2,187 control vocabulary words in Longman's Dictionary of Contem- porary English (LDOCE) [Procter, 1978] based on the co-occurrence of words in LDOCE dictionary definitions. These neighborhoods were then used to expand the word sense definitions of the word to be disambiguated, and the'overlap between the expanded definitions and the lo- cdl context was used to select the correct sense of a word. •/~ similar method reported by Guthrie et al., [1991] de- fined subject-specific neighborhoods of words, using the subject area markings in the machine readable version of LDOCE. Hearst [1991] suggests using syntactic informa- tion and part-of-speech tagging to aid in the disambigua- tion. She gathers co-occurrence information from man- ually sense-tagged text. Zernik and Jacobs [1990] also derive their neighborhoods from a training text which has been sense-tagged by hand. This method incorpo- rates other clues as to the sense of the word in question found in the morphology or by first tagging the text as to part of speech. Although each of these techniques looks somewhat promising for disambiguation, the techniques have only been applied to several words, and the results have been based on experiments which repeatedly disambiguate a single word (or in [Zernik …

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