Iterative Search using Query Aspects
Manmeet Mahinderjit Singh, W. Bruce Croft · 2016
Pseudo-relevance feedback (PRF) via query expansion has proven to be effective in many information retrieval tasks. In most existing work, the top-ranked documents from an initial search are assumed to be relevant and used for feedback. There are some drawbacks to this approach. One limitation is that there might be other relevant documents which were not retrieved or considered for the the feedback process. Another issue is one or more of the top retrieved documents may be non-relevant, which can introduce noise into the feedback mechanism. Term-level diversification, on the other hand, uses an effective technique for identifying terms associated with query aspects or subtopics. We propose a new iterative feedback method that combines PRF with aspect generation to improve feedback effectiveness. In our experiments, we discovered a new property of convergence of feedback terms that was incorporated into the PRF process. We show that the resulting method significantly outperforms the baseline relevance model.