Computational lexical approaches to disambiguation divide into syntactic category assignment such as whether farm is a noun or a verb (Milne, 1986) and word sense disambiguation within syntactic category.9 The latter problem is the subject of this chapter. Assuming that word senses are listed together under one lexical entry in a given syntactic category, the problem is to select the correct one. One computational method of disambiguation is pattern matching where the surrounding words frequently associated with a sense are used to disambiguate a word. Such methods are powerful and can be used to eliminate 70% of the ambiguity (Black, 1986). A second method employs a rich syntactic lexicon which includes selectional restrictions (Gross, 1985). A third method uses a combination of structural and conceptual analysis for disambiguation (Black, 1986). In the present work a method is proposed which combines three types of information to disambiguate: fixed and frequent phrases, syntactic information and commonsense reasoning. It is similar to Black’s approach, but it differs in using a psycholinguistically motivated word meaning representations as the basis of a generalized disambiguation procedure. The advantage of the method is that it employs computationally expensive commonsense reasoning only for the difficult cases, and not for simpler cases.