Comparing usability of matching techniques for normalising biomedical named entities.
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.
Wang
Xinglong
X
School of Informatics, University of Edinburgh Edinburgh, EH8 9LW, UK. xwang@inf.ed.ac.uk
Matthews
Michael
M
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