Disambiguation of entities in MEDLINE abstracts by combining MeSH terms with knowledge

摘要

Entity disambiguation in the biomedical domain is an essential task in any text mining pipeline. Much existing work shares one limitation, in that their model training prerequisite and/or runtime computation are too expensive to be applied to all ambiguous entities in real-time. We propose an automatic, light-weight method that processes MEDLINE abstracts at large-scale and with high-quality output. Our method exploits MeSH terms and knowledge in UMLS to first identify unambiguous anchor entities, and then disambiguate remaining entities via heuristics. Experiments showed that our method is 79.6% and 87.7% accurate under strict and relaxed rating schemes, respectively. When compared to MetaMap's disambiguation, our method is one order of magnitude faster with a slight advantage in accuracy.