BioCreAtIvE task1A: entity identification with a stochastic tagger

• Published in: BMC bioinformatics Vol. 6 Suppl 1; no. S1; p. S4
• Main Authors: Kinoshita, Shuhei, Cohen, K Bretonnel, Ogren, Philip V, Hunter, Lawrence
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 24.05.2005; BioMed Central; BMC
• Subjects: Databases, Genetic - classification; Stochastic Processes; Vocabulary, Controlled
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-6-S1-S4

Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system. Like Tanabe and Wilbur, we approached the problem as one of part-of-speech tagging, adding a GENE tag to the standard tag set. Where their system uses the Brill tagger, we used TnT, the Trigrams 'n' Tags HMM-based part-of-speech tagger. Based on careful error analysis, we implemented a set of post-processing rules to correct both false positives and false negatives. We participated in both the open and the closed divisions; for the open division, we made use of data from NCBI. Our base system without post-processing achieved a precision and recall of 68.0% and 77.2%, respectively, giving an F-measure of 72.3%. The full system with post-processing achieved a precision and recall of 80.3% and 80.5% giving an F-measure of 80.4%. We achieved a slight improvement (F-measure = 80.9%) by employing a dictionary-based post-processing step for the open division. We placed third in both the open and the closed division. Our results show that a part-of-speech tagger can be augmented with post-processing rules resulting in an entity identification system that competes well with other approaches.