Proximity-Based Good Turing Discounting and Kernel Functions for Pseudo-Relevance Feedback

Abstract

During the last few years, it has become abundantly clear that the technological advances in information technology have led to the dramatic proliferation of information on the web and this, in turn, has led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play an essential role in retrieving the desired information, it becomes necessary to give more importance to the sites and topics where these new words appear, or rather, to give value to the words that occur frequently with them. For this purpose, in this paper, the authors propose a new robust correlation measure that assesses the relatedness of words for pseudo-relevance feedback. It is based on the co-occurrence and closeness of terms, and aims to select the appropriate words that best capture the user information need. Extensive experiments have been conducted on the OHSUMED test collection and the results show that the proposed approach achieves a considerable performance improvement over the baseline.