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. 2005;6 Suppl 1(Suppl 1):S6.
doi: 10.1186/1471-2105-6-S1-S6. Epub 2005 May 24.

Identifying gene and protein mentions in text using conditional random fields

Ryan McDonald  1 , Fernando Pereira
Affiliations

Identifying gene and protein mentions in text using conditional random fields

Ryan McDonald et al. BMC Bioinformatics. 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.

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Figures

Figure 1
Figure 1
Gene identification as a tagging problem. A sample tagging of a sentence using the beginning, inside and outside tag labels. The sentence has two gene mentions, Varicella-zoster virus (VZV) glycoprotein gI and type 1 transmembrane gylcoprotein.
See this image and copyright information in PMC

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