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This article is part of the supplement: A critical assessment of text mining methods in molecular biology

Open Access Report

Identifying gene and protein mentions in text using conditional random fields

Ryan McDonald* and Fernando Pereira

Author affiliations

Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania, USA, 19104

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Citation and License

BMC Bioinformatics 2005, 6(Suppl 1):S6  doi:10.1186/1471-2105-6-S1-S6

Published: 24 May 2005

Abstract

Background

We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t|o) of a tag sequence given an observation sequence directly, and have previously been employed successfully for other tagging tasks. The mechanics of CRFs and their relationship to maximum entropy are discussed in detail.

Results

We employ a diverse feature set containing standard orthographic features combined with expert features in the form of gene and biological term lexicons to achieve a precision of 86.4% and recall of 78.7%. An analysis of the contribution of the various features of the model is provided.