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http://pages.cs.wisc.edu/~bsettles/abner/
Description:
ABNER is a software tool for molecular biology text analysis. It began as a user-friendly interface for a system developed as part of the NLPBA/BioNLP 2004 Shared Task challenge. The details of that system are described in the paper below (Settles, 2004). At ABNER's core is a statistical machine learning system using linear-chain conditional random fields (CRFs) with a variety of orthographic and contextual features. Version 1.5 includes two models trained on the NLPBA and BioCreative corpora, for which performance is roughly state of the art (F1 scores of 70.5 and 69.9 respectively). The new version also includes a Java API allowing users to incorporate ABNER into their systems, as well as train and use models for other data.
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Citations:
B. Settles (2005). ABNER: an open source tool for automatically tagging genes, proteins, and other entity names in text. Bioinformatics, 21(14):3191-3192., 2005.
B. Settles (2004). Biomedical Named Entity Recognition Using Conditional Random Fields and Rich Feature Sets. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA), Geneva, Switzerland, pages 104-107.
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