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基于浅层句法特征的评价对象抽取研究

徐冰 赵铁军 王山雨 郑德权

徐冰, 赵铁军, 王山雨, 郑德权. 基于浅层句法特征的评价对象抽取研究. 自动化学报, 2011, 37(10): 1241-1247. doi: 10.3724/SP.J.1004.2011.01241
引用本文: 徐冰, 赵铁军, 王山雨, 郑德权. 基于浅层句法特征的评价对象抽取研究. 自动化学报, 2011, 37(10): 1241-1247. doi: 10.3724/SP.J.1004.2011.01241
XU Bing, ZHAO Tie-Jun, WANG Shan-Yu, ZHENG De-Quan. Extraction of Opinion Targets Based on Shallow Parsing Features. ACTA AUTOMATICA SINICA, 2011, 37(10): 1241-1247. doi: 10.3724/SP.J.1004.2011.01241
Citation: XU Bing, ZHAO Tie-Jun, WANG Shan-Yu, ZHENG De-Quan. Extraction of Opinion Targets Based on Shallow Parsing Features. ACTA AUTOMATICA SINICA, 2011, 37(10): 1241-1247. doi: 10.3724/SP.J.1004.2011.01241

基于浅层句法特征的评价对象抽取研究

doi: 10.3724/SP.J.1004.2011.01241
详细信息
    通讯作者:

    徐冰 博士研究生.主要研究方向为自然语言处理和情感分析. E-mail: xb@mtlab.hit.edu.cn

Extraction of Opinion Targets Based on Shallow Parsing Features

  • 摘要: 随着网络评论文本数量的快速增长,文本情感分析越来越受到研究者的广泛关注. 句子级文本情感分析就是对主观性文本进行细粒度的挖掘,有重要的研究价值. 评论句中的评价对象抽取是句子级情感分析要研究的关键问题之一. 为了提高评价对象抽取的性能,本文提出在系统模型的训练过程中引入浅层句法信息和启发式位置信息,同时在不增加领域词典的情况下, 有效提高系统的精确率.实验结果表明,将本文提出的特征引入到条件随机域模型和对比模型后,系统的各项指标均有所提高, 并且条件随机域模型的结果优于对比模型.同时,将条件随机域模型的结果与2008年国内中文评测的最大值比较,其F值超过最大值 5%.
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  • 收稿日期:  2010-11-22
  • 修回日期:  2011-03-24
  • 刊出日期:  2011-10-20

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