This paper describes a heuristic-based approach to word-sense disambiguation. The heuristics that are applied to disambiguate a word depend on its part of speech, and on its relationship to neighboring salient words in the text. Parts of speech are found through a tagger, and related neighboring words are identified by a phrase extractor operating on the tagged text. To suggest possible senses, each heuristic draws on semantic relations extracted from a Webster's dictionary and the semantic thesaurus WordNet. For a given word, all applicable heuristics are tried, and those senses that are rejected by all heuristics are discarded. In all, the disambiguator uses 39 heuristics based on 12 relationships.

Haase This paper describes the architecture, implementation and evaluation of NetSerf, a program for finding information archives on the Internet using natural language queries.NetSerf's query processor extracts structured, disambiguated representations from the queries.The query representations are matched to hand-coded representations of the archives using semantic knowledge from WordNet (a semantic thesaurus) and an on-line Webster's dictionary.NetSerf has been tested using a set of questions and answers developed independently for a game called Internet Hunt.The paper presents results comparing the performance of NetSerf and the standard IRs ystem SMART on this set of queries.