A Two-Stage Ranking Scheme for Pseudo Relevance Feedback
Rong Yan, Guanglai Gao · 2016
As for the majority methods of Pseudo Relevance Feedback (PRF), the document in pseudo relevant set is generally divided into the relevant and the non-relevant according to user query. It is so coarse that the lower robustness of PRF, because there is still some relevant information in the non-relevant document and non-relevant information in the relevant document. A novel ranking scheme is proposed in this paper in order to accomplish a higher quality of pseudo relevant set. We try to realize automatically topic content analysis for pseudo relevant set, and divide pseudo relevant set into the relevant and the non-relevant at the document content level, so as to extract semantic relevant content for further selecting good expansion terms based on a smaller granularity, which would not worry about the cases that the top-ranked documents contain very few relevant documents. The experimental results on real Chinese collection show that our scheme can significantly improve the performance of retrieval.