关键词: 主题模型, 主题内容, 伪相关反馈

Abstract: Traditional pseudo relevance feedback (PRF) algorithms use the document as a unit to extract words for query expansion, which will increase the noise of expansion source due to the larger extraction unit. This paper exploits the topic analysis techniques so as to alleviate the low quality of expansion source condition. Obtain semantic information hidden in the content of each document of pseudo-relevant set, and extract the abstract topic content information according to the relevance of the user query, which is described as a basic extraction unit to be used for query expansion. Compared with the traditional PRF algorithms and the PRF based on topic model algorithm, the experimental results on NTCIR 8 dataset show that the scheme in this paper can effectively extract more appropriate expansion terms. In addition, the results also show that the scheme in this paper has a positive impact to improve the retrieval performance on a smaller topic content granularity level.

Key words: topic model, topic content, pseudo relevance feedback (PRF)