BMC Bioinformatics

official impact factor 3.03
Search for
Advanced search
BMC Bioinformatics
Tools
Share this article

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

Open Access Report

Gene/protein name recognition based on support vector machine using dictionary as features

Tomohiro Mitsumori1*, Sevrani Fation1*, Masaki Murata2*, Kouichi Doi1* and Hirohumi Doi1

Author Affiliations

1 Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma-shi, Nara, 630-0101, Japan

2 National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0289, Japan

For all author emails, please log on.

BMC Bioinformatics 2005, 6(Suppl 1):S8 doi:10.1186/1471-2105-6-S1-S8

Published: 24 May 2005

Abstract

Background

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition.

Results

In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting.

Conclusion

During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.