随着社交媒体的发展及成熟，每天在互联网环境中都会产生大量的用户评论信息。抽取评价短语、评价对象和观点持有者等情感要素，已经成为了中文观点挖掘和情感分析的重要先决任务。针对中文情感要素抽取任务，本文提出了一个统计和规则相结合的级联模型，主要贡献包括：（1）针对汽车领域评论信息，构建情感要素标注语料库和相关词典；（2）对于以往研究较少关注的中文评价短语，本文详细分析阐述其定义和分类；（3）结合统计和规则，分别针对评价短语和情感要素提出级联抽取策略。实验结果充分证明了该级联模型的有效性，相比较于其它基于规则的情感要素抽取算法有效提升了召回率，同时为后续社交媒体情感分析任务提供了有力的支持。%With the development of social media,massive reviews are generated by users every day.The extraction of evaluation elements,including evaluation phrase,comment target and opinion holder,is an important pre-task of Chinese o-pinion mining and sentiment analysis.This paper proposes an efficient method for extracting Chinese evaluation elements based on cascaded model and mainly makes three contributions:(i)to implement and evaluate the method,we construct an original annotated corpus for Chinese evaluation elements of automobile;(ii)we provide specific definition and classifica-tion of Chines evaluation phrase;(iii)combing statistic method and rule-based method,we present cascaded strategy for ex-traction of evaluation phrase and evaluation elements,respectively.According to the experiment results,the proposed method performs well,and effectively improve the recall compared with other rule-based algorithm.Meanwhile it contributes greatly to our subsequent tasks,such as sentiment analysis of social media.
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