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In the biomedical domain, word sense ambiguity is a widely spread problem with bioinformatics research effort devoted to it being not commensurate and allowing for more development. This paper presents and evaluates a learning-based approach for sense disambiguation within the biomedical domain. The main limitation with supervised methods is the need for a corpus of manually disambiguated instances of the ambiguous words. However, the advances in automatic text annotation and tagging techniquesdoi:10.1100/2012/949247 pmid:22666174 pmcid:PMC3361294 fatcat:2vx3kqpbsbb53c6bdusnb34tde