基于关联度模型的文本倾向性检索研究

Research on Chinese Opinion Retrieval Based on Relevance Model

该文在研究了信息检索理论与文本倾向性分析技术等的基础上,结合国内外关于观点检索的相关研究,提出了基于关联度的文本观点检索算法。它综合考虑了主题检索过程中的查询扩展、文本检索相关度、文本倾向性强度和检索主题与文本情感的关联度等对观点检索最后结果的影响。该算法从理论上考虑了观点检索不同因素之间的相互影响问题。通过对COAE2008观点检索子任务的实验数据进行实验,结果表明:该文提出的基于关联度的观点检索算法可以取得较好的效果。

In this paper,we present an opinion retrieval algorithm that retrieves opinions for a given topic according to the relevance between the topic and its expansions,the topic and the sentiments and so on.It＇s based on the theory of information retrieval,the sentiment analysis and the research for opinion retrieval of other researchers.The algorithm uses relevance to measure the affects to the topic,such as the expansions of the topic,the texts,the sentiments in the text,and other elements for finding the opinions,which integrates the influence among all the elements of this research theoretically.Experimental results on COAE2008 datasets and queries show that the algorithm is effective and gets a higher score than other methods for opinion retrieval.