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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
Publication Date: 2011/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
hedges, voting, classification, machine learning,
Full Text: PDF(392.6KB)
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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.