title: Comparing usability of matching techniques for normalising biomedical named entities.
abstract: String matching plays an important role in biomedical Term Normalisation, the task of linking mentions of biomedical entities to identifiers in reference databases. This paper evaluates exact, rule-based and various string-similarity-based matching techniques. The matchers are compared in two ways: first, we measure precision and recall against a gold-standard dataset and second, we integrate the matchers into a curation tool and measure gains in curation speed when they were used to assist a curator in normalising protein and tissue entities. The evaluation shows that a rule-based matcher works better on the gold-standard data, while a string-similarity based system and exact string matcher win out on improving curation efficiency.
authors.affiliation: School of Informatics, University of Edinburgh Edinburgh, EH8 9LW, UK. xwang@inf.ed.ac.uk
authors.forename: Xinglong
authors.initials: X
authors.lastname: Wang
authors.forename: Michael
authors.initials: M
authors.lastname: Matthews
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D016208:Databases, Factual; D030562:Databases, Protein; D009323:Natural Language Processing; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data; D009626:Terminology as Topic