Exploiting and integrating rich features for biological literature classification.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Wang
Hongning
H
State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. whn03@mails.tsinghua.edu.cn
Huang
Minlie
M
Ding
Shilin
S
Zhu
Xiaoyan
X
D000465:Algorithms; D001185:Artificial Intelligence; D004014:Dictionaries as Topic; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D010506:Periodicals as Topic; D009626:Terminology as Topic; D018875:Vocabulary, Controlled