Species Disambiguation for Biomedical Term Identification

An important task in information extraction (IE) from biomedical articles is term identification (TI), which concerns linking entity mentions (e.g., terms denoting proteins) in text to unambiguous identifiers in standard databases (e.g., RefSeq). Previous work on TI has focused on species-specific documents. However, biomedical documents, especially full-length articles, often talk about entities across a number of species, in which case resolving species ambiguity becomes an indispensable part of ti. This paper describes our rule-based and machine-learning based approaches to species disambiguation and demonstrates that performance of TI can be improved by over 20% if the correct species are known. We also show that using the species predicted by the automatic species taggers can improve TI by a large margin.