TITLE

Bio Tagger-GM: A Gene/Protein Name Recognition System

AUTHOR(S)
TORII, MANABU; ZHANGZHI HU; WU, CATHY H.; HONGFANG LIU
PUB. DATE
March 2009
SOURCE
Journal of the American Medical Informatics Association;Mar/Apr2009, Vol. 16 Issue 2, p247
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Objectives: Biomedical named entity recognition (BNER) is a critical component in automated systems that mine biomedical knowledge in free text. Among different types of entities in the domain, gene/protein would be the most studied one for BNER. Our goal is to develop a gene/protein name recognition system BioTagger-GM that exploits rich information in terminology sources using powerful machine learning frameworks and system combination. Design: BioTagger-GM consists of four main components: (1) dictionary lookup—gene/protein names in BioThesaurus and biomedical terms in UMLS Metathesaurus are tagged in text, (2) machine learning—machine learning systems are trained using dictionary lookup results as one type of feature, (3) post-processing—heuristic rules are used to correct recognition errors, and (4) system combination—a voting scheme is used to combine recognition results from multiple systems. Measurements: The BioCreAtIvE II Gene Mention (GM) corpus was used to evaluate the proposed method. To test its general applicability, the method was also evaluated on the JNLPBA corpus modified for gene/protein name recognition. The performance of the systems was evaluated through cross-validation tests and measured using precision, recall, and F-Measure. Results: BioTagger-GM achieved an F-Measure of 0.8887 on the BioCreAtIvE II GM corpus, which is higher than that of the first-place system in the BioCreAtIvE II challenge. The applicability of the method was also confirmed on the modified JNLPBA corpus. Conclusion: The results suggest that terminology sources, powerful machine learning frameworks, and system combination can be integrated to build an effective BNER system.
ACCESSION #
36948134

 

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