Lexical disambiguation using simulated annealing

Abstract

The resolution of lexical ambiguity is important for most natural 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 describe 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 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.

A semantic approach for text clustering using WordNet and lexical chains

“…Early work varied between counting word overlaps between definitions of the word Cowie, Guthrie, & Guthrie, 1992;Kilgarriff & Rosenzweig, 2000;Lesk, 1986) to finding distances between concepts following the structure of the LKB (Patwardhan, Banerjee, & Pedersen, 2003). As an alternative, graph-based methods have gained much attention in recent years (Agirre & Soroa, 2009;Mihalcea, 2005;Navigli & Lapata, 2010;Navigli & Velardi, 2005;Ponzetto & Navigli, 2010;Sinha & Mihalcea, 2007).…”

Using semantic techniques to access web data

“…In all the evaluations, the disambiguation has been performed over all the terms contained in the snippets. This choice was due to the fact that the disambiguation of one word can affect others in its context [59]. Disambiguating the keywords only is unfeasible, as the keywords need the context where they are located in order to be correctly disambiguated.…”

Hybrid models for sense guessing of Chinese unknown words

“…Knowledge-based approaches make use of Machine Readable Dictionaries (MRDs) (e.g. Cowie et al 1992;Lesk 1986), thesauri (e.g. Yarowsky 1992), computational lexicons (e.g.…”