论文类型：期刊论文
发表刊物：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
收录刊物：EI、SCIE、Scopus
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.