title: A novel feature selection strategy for enhanced biomedical event extraction using the Turku system.
abstract: Feature selection is of paramount importance for text-mining classifiers with high-dimensional features. The Turku Event Extraction System (TEES) is the best performing tool in the GENIA BioNLP 2009/2011 shared tasks, which relies heavily on high-dimensional features. This paper describes research which, based on an implementation of an accumulated effect evaluation (AEE) algorithm applying the greedy search strategy, analyses the contribution of every single feature class in TEES with a view to identify important features and modify the feature set accordingly. With an updated feature set, a new system is acquired with enhanced performance which achieves an increased F-score of 53.27% up from 51.21% for Task 1 under strict evaluation criteria and 57.24% according to the approximate span and recursive criterion.
authors.affiliation: College of Science, Huazhong Agricultural University, Wuhan, Hubei 430070, China ; Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Jingbo
authors.initials: J
authors.lastname: Xia
authors.affiliation: Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong ; The Halliday Centre for Intelligent Applications of Language Studies, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Alex Chengyu
authors.initials: AC
authors.lastname: Fang
authors.affiliation: Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong ; The Halliday Centre for Intelligent Applications of Language Studies, City University of Hong Kong, Kowloon, Hong Kong.
authors.forename: Xing
authors.initials: X
authors.lastname: Zhang
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D000465:Algorithms; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012660:Semantics; D018875:Vocabulary, Controlled