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论文类型：期刊论文

发表时间：2011-10-01

发表刊物：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

收录刊物：Scopus、SCIE、EI

卷号：E94D

期号：10

页面范围：1989-1997

ISSN号：0916-8532

关键字：hedges; voting; classification; machine learning

摘要：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.