基于词汇语义和句法依存的情感关键句识别

Sentiment Key Sentence Identification Based on Lexical Semantics and Syntactic Dependency

门户网站、博客和论坛中的新闻性文章往往都带有自己的情感倾向性,而情感关键句的识别对判断文章的情感倾向、了解社会动态和舆情状况有着非常重要的作用.传统方法主要基于词汇特征,未能充分利用潜在的句法和语义信息.本文提出了一种基于词汇语义和句法依存的情感关键句识别方法.该方法首先通过构建情感词典和关键词词典获取词汇语义信息,然后利用一种新颖的面向情感关键句提取算法获取句法依存信息,最后把情感关键句的识别问题看成一个是否为情感关键句的二分类问题加以解决.在COAE2014公开评测数据集上进行的实验表明本文方法的准确率和召回率均显著优于其他方法.

A lot of news articles in the portal,blog and forums always have their own emotional orientations and sentiment key sentence identification plays an important role in distinguishing emotional orientation of one article,supervising social trends and public sentiment state. The traditional lexicon-based methods totally depended on lexical semantics and did not excavate the implied syntactic structure. So a hybrid method of sentiment key sentence identification based on lexical semantics and syntactic dependency is proposed in this paper. This approach first gets lexical semantics knowledge from emotion lexicon expansion and keywords lexicon construction,and then this paper proposes a novel dependency templates extraction algorithm for syntactic dependency information to build a dependency knowledge base,finally we regard sentiment key sentence identification as a classification task and perform identification through different groups of features. Experimental results on COAE2014 dataset showthat this approach notably outperforms other baselines of sentiment key sentence identification on precision and recall.