Abstract

Re-ranking for Information Retrieval aims to elevate relevant feedbacks and depress negative ones in initial retrieval result list. Compared to relevance feedback-based re-ranking method widely adopted in the literature, this paper proposes a new method to well use three features in known negative feedbacks to identify and depress unknown negative feedbacks. The features include: 1) the minor (lower-weighted) terms in negative feedbacks; 2) hierarchical distance (HD) among feedbacks in a hierarchical clustering tree; 3) obstinateness strength of negative feedbacks. We evaluate the method on the TDT4 corpus, which is made up of news topics and their relevant stories. And experimental results show that our new scheme substantially outperforms its counterparts. 1.

Citations