This article is part of the supplement: Proceedings of the Second International Symposium on Languages in Biology and Medicine (LBM) 2007
Exploiting and integrating rich features for biological literature classification
- Equal contributors
State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
BMC Bioinformatics 2008, 9(Suppl 3):S4 doi:10.1186/1471-2105-9-S3-S4Published: 11 April 2008
Efficient features play an important role in automated text classification, which definitely facilitates the access of large-scale data. In the bioscience field, biological structures and terminologies are described by a large number of features; domain dependent features would significantly improve the classification performance. How to effectively select and integrate different types of features to improve the biological literature classification performance is the major issue studied in this paper.
To efficiently classify the biological literatures, we propose a novel feature value schema TF*ML, features covering from lower level domain independent “string feature” to higher level domain dependent “semantic template feature”, and proper integrations among the features. Compared to our previous approaches, the performance is improved in terms of AUC and F-Score by 11.5% and 8.8% respectively, and outperforms the best performance achieved in BioCreAtIvE 2006.
Different types of features possess different discriminative capabilities in literature classification; proper integration of domain independent and dependent features would significantly improve the performance and overcome the over-fitting on data distribution.