High-recall protein entity recognition using a dictionary.
CONCLUSIONS
Protein name extraction is an important step in mining biological literature. We describe two new methods for this task: semiCRFs and dictionary HMMs. SemiCRFs are a recently-proposed extension to conditional random fields (CRFs) that enables more effective use of dictionary information as features. Dictionary HMMs are a technique in which a dictionary is converted to a large HMM that recognizes phrases from the dictionary, as well as variations of these phrases. Standard training methods for HMMs can be used to learn which variants should be recognized. We compared the performance of our new approaches with that of Maximum Entropy (MaxEnt) and normal CRFs on three datasets, and improvement was obtained for all four methods over the best published results for two of the datasets. CRFs and semiCRFs achieved the highest overall performance according to the widely-used F-measure, while the dictionary HMMs performed the best at finding entities that actually appear in the dictionary-the measure of most interest in our intended application.


BACKGROUND
Dictionary HMMs were implemented in Java. Algorithms are available through an information extraction package MINORTHIRD on http://minorthird.sourceforge.net
Kou
Zhenzhen
Z
Center for Automated Learning and Discovery, Carnegie Mellon University Pittsburgh, PA 15213, USA. zkou@andrew.cmu.edu
Cohen
William W
WW
Murphy
Robert F
RF
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004014:Dictionaries as Topic; D019277:Entropy; D020407:Internet; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011381:Programming Languages; D040901:Proteomics / Q000379:methods*; D012984:Software; D018875:Vocabulary, Controlled