BioCreAtIvE task1A: entity identification with a stochastic tagger.
BACKGROUND
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.


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 third in both the open and the closed division.


CONCLUSIONS
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.
Kinoshita
Shuhei
S
Center for Computational Pharmacology, University of Colorado School of Medicine, Denver, Colorado, USA. kino@strad.ssg.fujitsu.com
Cohen
K Bretonnel
KB
Ogren
Philip V
PV
Hunter
Lawrence
L
D030541:Databases, Genetic / Q000145:classification*; D013269:Stochastic Processes; D018875:Vocabulary, Controlled