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

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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.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(10), 1989-1997, 2011-10-01

    一般社団法人 電子情報通信学会

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各種コード

  • NII論文ID(NAID)
    10030193461
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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