BioCreAtIvE Task1A: entity identification with a stochastic tagger

Background: Our approach to Task 1A was inspired by Tanabe and Wilbur's ABGene system [1,2]. 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 [3]. 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. Results: 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 thi...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Shuhei Kinoshita, K. Bretonnel Cohen, Philip V. Ogren, Lawrence Hunter
• Keywords: BMCBI 2005, Entity Identification, HMM-based Part-of-speech Tagger, Part-of-speech Tagger