| 基于依存关系和最大熵的特征-情感对分类 |
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| 引用本文： | 张磊,李珊,彭舰,陈黎,黎红友.基于依存关系和最大熵的特征-情感对分类[J].电子科技大学学报（自然科学版）,2014,43(3):420-425. |
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| 作者姓名： | 张磊 李珊 彭舰 陈黎 黎红友 |
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| 作者单位： | 1.四川大学计算机学院 成都 610065; |
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| 基金项目： | 国家自然科学基金面上项目(71372189); 国家自然科学基金(61363019) |
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| 摘 要： | 中文产品评论特征词与关联的情感词的分类是观点挖掘的重要研究内容之一. 该文改进了英文依存关系语法，总结出5种常用的中文产品评论依存关系; 利用最大熵模型进行训练，设计了基于依存关系的复合特征模板. 实验证明，应用该复合模板进行特征-情感对的提取，系统的查全率和F-score相比于传统方法，分别提高到78.68%和75.36%.
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| 关 键 词： | 依存关系 特征-情感对 特征模板 最大熵 Web数据挖掘 |
| 收稿时间： | 2012-12-03 |
Feature-Opinion Pairs Classification Based on Dependency Relations and Maximum Entropy Model |
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| Affiliation: | 1.School of Computer Science,Sichuan University Chengdu 610065;2.School Business,Sichuan University Chengdu 610065 |
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| Abstract: | In recent years, feature-opinion pairs classification of Chinese product review is one of the most important research field in Web data mining technology. In this paper, five types of Chinese dependency relationships for product review have been concluded based on the traditional English dependency grammar. The maximum entropy model is used to predict the opinion-relevant product feature relations. To train the model, a set of feature symbol combinations have been designed by means of Chinese dependency. The experiment result shows that the recall and F-score of our approach could reach 78.68% and 75.36% respectively, which is clearly superior to Hu's adjacent based method and Popesecu's pattern based method. |
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