Recognizing Named Entity from Free-text Customer Reviews——A Maximum Entropy Model-based Approach
Yu Chuanming1,2, Huang Jianqiu2, Guo Fei2
1. School of Information Safety and Engineering, Zhongnan University of Economics and Law, Wuhan 430073, China;
2. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Abstract：This paper introduces the concept of Named Entity Recognition (NER), analyzes two basic approaches, the rule-based approach and the statistical approach, and conducts an empirical study on Chinese dish name recognition based on the theory of Maximum Entropy Model (MEM). According to the characteristics of Chinese named entity, 6 feature templates are designed. Experimental results show that adding tagging features to the basic simple feature template can efficiently improve the performance of Named Entity Recognition. The features in order to improve recognition performance are as follow: tagging features, combination of POS features, forward POS dependency features and word form features.
余传明, 黄建秋, 郭飞. 从客户评论中识别命名实体——基于最大熵模型的实现[J]. 现代图书情报技术, 2011, 27(5): 77-82.
Yu Chuanming, Huang Jianqiu, Guo Fei. Recognizing Named Entity from Free-text Customer Reviews——A Maximum Entropy Model-based Approach. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2011.05.12.
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