This article is part of the supplement: A critical assessment of text mining methods in molecular biology
Exploring the boundaries: gene and protein identification in biomedical text
1 Department of Computer Science, Stanford University, Stanford CA 94305-9040, USA
2 Institute for Communicating and Collaborative Systems, University of Edinburgh, United Kingdom
BMC Bioinformatics 2005, 6(Suppl 1):S5 doi:10.1186/1471-2105-6-S1-S5Published: 24 May 2005
Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.
We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.
This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.
Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.