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山东大学学报（理学版） ›› 2016, Vol. 51 ›› Issue (1): 58-64.doi: 10.6040/j.issn.1671-9352.1.2015.035

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基于词对齐模型的中文评价对象与评价词抽取

陈兴俊,魏晶晶,廖祥文*,简思远,陈国龙   

  1. 福州大学数学与计算机科学学院, 福建 福州 350116
  • 收稿日期:2015-09-25 出版日期:2016-01-16 发布日期:2016-11-29
  • 通讯作者: 廖祥文(1980— ),男,副教授,研究方向为网络文本倾向性分析.E-mail: liaoxw@fzu.edu.cn E-mail:mrchenxj@qq.com
  • 作者简介:陈兴俊(1989— ),男,硕士研究生,研究方向为面向社会媒介的观点挖掘.E-mail:mrchenxj@qq.com
  • 基金资助:
    国家自然科学基金青年项目(61300105);教育部博士点基金联合资助项目(2012351410010);福建省科技重大专项项目(2013H6012);福州市科技计划项目(2012-G-113,2013-PT-45)

Extraction of opinion targets and opinion words from Chinese sentences based on word alignment model

CHEN Xing-jun, WEI Jing-jing, LIAO Xiang-wen*, JIAN Si-yuan, CHEN Guo-long   

  1. College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, Fujian, China
  • Received:2015-09-25 Online:2016-01-16 Published:2016-11-29

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摘要： 提出一种基于统计机器翻译的思想抽取评价对象与评价词的方法。该方法利用词对齐模型抽取评价对象与评价词之间的关系,并结合词共现信息等特征来估计两者关系的强度。建立一张二分图刻画评价关系,并加入领域相关性度量,利用随机游走算法迭代计算候选评价对象与评价词的置信度。在COAE2011任务3的语料上进行试验验证。结果表明,利用词对齐模型抽取评价对象与评价词可以有效提高准确度,抽取出更多的评价对象与评价词。

关键词: 评价对象抽取, 中文句子, 评价词抽取, 词对齐模型

Abstract: This paper proposed an approach to extract opinion targets and opinion words based on the idea of statistical machine translation. This method extracted the associations between opinion targets and opinion words by using word alignment model, whose strength was estimated with word co-occurrence. To model these associations, the approach constructed a bipartite graph. Then a domain relevance measure was used, and random-walk algorithm was applied to calculate the confidence of each opinion target candidates and opinion word candidates. The method was evaluated on the labeled corpus of task 3 in COAE2011.The experiment results showed that it could effectively improve the accuracy by employing the model of word-alignment to extract opinion targets and opinion words, and the method could extract more targets and words simultaneously.

Key words: opinion targets extraction, Chinese sentence, word-alignment model, opinion word extraction

中图分类号: 

  • TP391
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