Structure Cognizant Pseudo Relevance Feedback

We propose a structure cognizant framework for pseudo relevance feedback (PRF). This has an application, for example, in selecting expansion terms for general search from subsets such as Wikipedia, wherein documents typically have a minimally fixed set of fields, viz., Title, Body, Infobox and Categories. In existing approaches to PRF based expansion, weights of expansion terms do not depend on their field(s) of origin. This, we feel, is a weakness of current PRF approaches. We propose a per field EM formulation for finding the importance of the expansion terms, in line with traditional PRF. However, the final weight of an expansion term is found by weighting these importance based on whether the term belongs to the title, the body, the infobox or the category field(s). In our experiments with four languages, viz., English, Spanish, Finnish and Hindi, we find that this structure-aware PRF yields a 2% to 30% improvement in performance (MAP) over the vanilla PRF. We conduct ablation tests to evaluate the importance of various fields. As expected, results from these tests emphasize the importance of fields in the order of title, body, categories and infobox.