Lexical Disambiguation using Simulated Annealing

Lexical Disambiguation using Simulated Annealing Jim Cowie, Joe Guthrie* and Louise Guthrie Computing Research Laboratory Box 30001 New Mexico State University Las Cruces, NM 88003-0(301 ABSTRACT The resolution of lexical ambiguity is important for most natural language process- ing tasks, and a range of computational techniques have been proposed for its solu- tion. None of these has yet proven effective on a large scale. In this paper, we describe a method for lexical disambiguation of text using the definitions in a machine-readable dictionm~j 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 Dic- tionary of Contemporary 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. L Introduction The problem of word-sense disambi- guation is central to text processing. Recently, promising computational methods have been suggested [Lesk, 1986; McDonald * Present address: Mathematics DepaJtment. University of Texas at El Paso, El Paso, Tx 79968 et al., 1990; Veronis and Ide, 1990; Wilks et al., 1990; Zernik and ,lacobs, 1990; Guthrie et al., 1991; Hearst, 1991] which attempt to use the local context of the word to be disambiguated together with information about each of its word senses to solve this problem. Lesk [1986] described a technique which measured the amount of overlap between a dictionary sense definition and the definitions of the words in the local con- text of the word to be disambiguated. He illustrated his method by successfully disambiguating 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 con- trol vocabulary words in Longman's Dic- tionary of Contemporaay 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 local context was used to select the correct sense of a word. A similar method reported by Guthrie et al., [1991] who defined subject specific neighborhoods of words, using the subject area markings in the machine read- able version of LDOCE. Hearst [1991] sug- gests using syntactic information and part- of-speech tagging to aid in the disambigua- tton. She gathers co-occurrence information Aclxs DE COLING-92. NANTES, 23-28 AOt]T 1992 3 5 9 PROC, OV COLING-92, NAN'rES, AUG. 23-28, 1992 from manually senseutagged text. Zemik and Jacobs [1990] also derive their neigh- borhoods from a training text which has I~en sense-tagged by hand. Their method incorporates other clues as to the sense of the word in question found in …

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