基于知识图谱的文本观点检索方法

文本观点检索旨在检索出与查询主题相关并且表达用户对主题观点的文档。由于用户查询时输入通常很短,难以准确表示查询的信息需求。知识图谱是结构化的语义知识库,通过知识图谱中的知识有助于理解用户的信息需求。因此,提出了一种基于知识图谱的文本观点检索方法。首先由知识图谱获取候选查询扩展词,并计算每个候选词扩展词分布、共现频率、邻近关系、文档集频率,然后利用4类特征通过SVM分类得到扩展词,最后利用扩展词对产生式观点检索模型进行扩展,实现对查询的观点检索。实验表明,在微博和推特两个数据集上,与基准工作对比,所提出的方法在MAP、NDCG等评价指标上均有显著的提升。

A text opinion retrieval method based on knowledge graph

Text opinion retrieval aims at finding relevant and opinionate documents according to a users query. User queries are usually too short to describe the information need accurately. Knowledge Graph is a structured semantic knowledge base, which the information of knowledge graph can help us to describe the information need. In this paper, we propose a text opinion retrieval method based on knowledge graph. Firstly, we get the candidate of query expansion terms by knowledge graph, and calculate four kinds of features of each candidate named term distributions, co-occurrence frequency, proximity and collection frequency. Then, we choose the expansion terms by SVM classifier with the features. Finally, we expand the generative opinion retrieval model using the expansion terms to get the opinion retrieval result. Experimental results on Sina Microblog and Twitter datasets show that our proposed method obtains significant improvements in terms of MAP and NDCG over the baseline approaches.