Generating quality word sense disambiguation test sets based on MeSH indexing.

Word sense disambiguation (WSD) determines the correct meaning of a word that has more than one meaning, and is a critical step in biomedical natural language processing, as interpretation of information in text can be correct only if the meanings of their component terms are correctly identified first. Quality evaluation sets are important to WSD because they can be used as representative samples for developing automatic programs and as referees for comparing different WSD programs. To help create quality test sets for WSD, we developed a MeSH-based automatic sense-tagging method that preferentially annotates terms being topical of the text. Preliminary results were promising and revealed important issues to be addressed in biomedical WSD research. We also suggest that, by cross-validating with 2 or 3 annotators, the method should be able to efficiently generate quality WSD test sets. Online supplement is available at: http://www.dbmi.columbia.edu/~juf7002/AMIA09.