A method of sense resolution is proposed that is based on WordNet, an on-line lexical database that incorporates semantic relations (synonymy, antonymy, hyponymy, meronymy, causal and troponymic entailment) as labeled pointers between word senses. With WordNet, it is easy to retrieve sets of semantically related words, a facility that will be used for sense resolution during text processing, as follows. When a word with multiple senses is encountered, one of two procedures will be followed. Either, (1) words related in meaning to the alternative senses of the polysemous word will be retrieved; new strings will be derived by substituting these related words into the context of the polysemous word; a large textual corpus will then be searched for these derived strings; and that sense will be chosen that corresponds to the derived string that is found most often in the corpus. Or, (2) the context of the polysemous word will be used as a key to search a large corpus; all words found to occur in that context will be noted; WordNet will then be used to estimate the semantic distance from those words to the alternative senses of the polysemous word; and that sense will be chosen that is closest in meaning to other words occurring in the same context. If successful, this procedure could have practical applications to problems of information retrieval, mechanical translation, intelligent tutoring systems, and elsewhere.