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Indexed by:期刊论文

Date of Publication:2011-06-01

Journal:INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL

Included Journals:SCIE

Volume:14

Issue:6

Page Number:1969-1981

ISSN No.:1343-4500

Key Words:Text Mining; Gene Mention Tagging; Named Entity Recognition

Abstract:Gene mention tagging is one of the basic tasks in automatic information extraction from biomedical texts. It is still a challenge because of the irregularity of naming and the frequent appearing of new genes. In this paper, six divergent models are implemented with different machine learning algorithms and dissimilar feature sets. The recognition results from the six models are then combined using the simple set operation method (union and intersection) and the voting method to further improve tagging performance. Experiments conducted on the corpus of BioCreative II GM task show that our best performing integration model achieves an F-score of 88.10%, which outperforms most of the state-of-the-art systems.