Knowledge-based biomedical word sense disambiguation: comparison of approaches

Background: Word sense disambiguation (WSD) algorithms attempt to select the proper sense of ambiguous terms in text. Resources like the UMLS provide a reference thesaurus to be used to annotate the biomedical literature. Statistical learning approaches have produced good results, but the size of the UMLS makes the production of training data infeasible to cover all the domain. Methods: We present research on existing WSD approaches based on knowledge bases, which complement the studies performed on statistical learning. We compare four approaches which rely on the UMLS Metathesaurus as the source of knowledge. The first approach compares the overlap of the context of the ambiguous word to the candidate senses based on a representation built out of the definitions, synonyms and related terms. The second approach collects training data for each of the candidate senses to perform WSD based on queries built using monosemous synonyms and related terms. These queries are used to retrieve M...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Antonio Jimeno Yepes, Alan R. Aronson
• Keywords: Ambiguous Word, BMCBI 2010, UMLS, Word Sense Disambiguation