Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts

Huiwei ZHOU  Xiaoyan LI  Degen HUANG  Yuansheng YANG  Fuji REN  

IEICE TRANSACTIONS on Information and Systems   Vol.E94-D   No.10   pp.1989-1997
Publication Date: 2011/10/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E94.D.1989
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
hedges,  voting,  classification,  machine learning,  

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Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.