(A) study on the resolution of word-sense ambiguity with multiple knowledge structures

Abstract

Natural language processing systems repeatedly have to solve word-sense ambiguity problem. The word-sense ambiguity problem is the selection of the intended word-meaning of a word from the set of its possible meanings. To solve the problem, it is necessary to use several sources of knowledges such as lexical knowledge, morphlogical knowledge, syntactic knowledge, semantic knowledge, various kinds of contextual knowledge etc. This knowledge sources must concurrently not sequentially participate in parsing input sentence and must interact with each others to help disambiguating the word meanings. Considering this problem, in this thesis, a word meaning selection system is designed. By building a language comprehension model which maps input sentences ultimately into internal meaning representations, a knowledge-source based hierarchical multiprocess word meaning selection system is designed with respect to the model. It is partially implemented using the PEARL Al package. The formalization of word meaning selection in a context using schemata is also presented.