Departmental Papers (CIS)

Date of this Version

May 2005

Document Type

Journal Article

Comments

Reprinted from BMC Bioinformatics, Volume 6( Suppl 1), S6, May 24, 2005, 7 pages.

Abstract

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.

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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Date Posted: 20 July 2006

This document has been peer reviewed.