Learning the Species of Biomedical Named Entities from Annotated Corpora

In biomedical articles, terms with the same surface forms are often used to refer to different entities across a number of model organisms, in which case determining the species becomes crucial to term identification systems that ground terms to specific database identifiers. This paper describes a rule-based system that extracts `species indicating words', such as human or murine, which can be used to decide the species of the nearby entity terms, and a machine-learning species disambiguation system that was developed on manually speciesannotated corpora. Performance of both systems were evaluated on gold-standard datasets, where the machine-learning system yielded better overall results.

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Xinglong Wang, Claire Grover
• Keywords: Education, LREC 2008, Machine-learning Species Disambiguation, Nearby Entity Terms, Species Indicating Words