Introduction

Many words have two or more very distinct meanings. For example, the word pen can refer to a writing implement or to an enclosure. Many natural language applications, including information retrieval, content analysis and automatic abstracting, and machine translation, require the resolution of lexical ambiguity for words in an input text, or are significantly improved when this can be accomplished. That is, the preferred input to these applications is a text in which each word is “tagged” with the sense of that word intended in the particular context. However, at present there is no reliable way to automatically identify the correct sense of a word in running text. This task, called word sense disambiguation, is especially difficult for texts from unconstrained domains because the number of ambiguous words is potentially very large. The magnitude of the problem can be reduced by considering only very gross sense distinctions (e.g., between the pen-as-implement and pen-as-enclosure senses of pen, rather than between finer sense distinctions within, say, the category of pen-as-enclosure – i.e., enclosure for animals, enclosure for submarines, etc.), which is sufficient for many applications. But even so, substantial ambiguity remains: for example, even the relatively small lexicon (20,000 entries) of the TRUMP system, which includes only gross sense distinctions, finds an average of about four senses for each word in sentences from the Wall Street Journal (McRoy, 1992). The resulting combinatoric explosion demonstrates the magnitude of the lexical ambiguity problem.

Several different kinds of information can contribute to the resolution of lexical ambiguity.