Most previous corpus-based algorithms disambiguate a word with a classifier trained from previous usages of the same word.
Separate classifiers have to be trained for different words.
I present an algorithm that uses the same knowledge sources to disambiguate different words. The algorithm does not require a sense-tagged corpus and exploits the fact that two different words are likely to have similar meanings if they occur in identical local contexts. I will also present an information-theoretic measure of similarity that overcomes several problems with previous similarity measures. Links to Dr. Lin's research interests and papers Return to the UMD Computational Linguistics Colloquium Series .