Multilingual Pseudo-Relevance Feedback: Performance Study of Assisting Languages

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1346–1356, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Multilingual Pseudo-Relevance Feedback: Performance Study of Assisting Languages Manoj K. Chinnakotla Karthik Raman Pushpak Bhattacharyya Department of Computer Science and Engineering Indian Institute of Technology, Bombay, Mumbai, India {manoj,karthikr,pb}@cse.iitb.ac.in Abstract In a previous work of ours Chinnakotla et al. (2010) we introduced a novel framework for Pseudo-Relevance Feed- back (PRF) called MultiPRF. Given a query in one language called Source, we used English as the Assisting Language to improve the performance of PRF for the source language. MulitiPRF showed re- markable improvement over plain Model Based Feedback (MBF) uniformly for 4 languages, viz., French, German, Hungar- ian and Finnish with English as the as- sisting language. This fact inspired us to study the effect of any source-assistant pair on MultiPRF performance from out of a set of languages with widely differ- ent characteristics, viz., Dutch, English, Finnish, French, German and Spanish. Carrying this further, we looked into the effect of using two assisting languages to- gether on PRF. The present paper is a report of these in- vestigations, their results and conclusions drawn therefrom. While performance im- provement on MultiPRF is observed what- ever the assisting language and whatever the source, observations are mixed when two assisting languages are used simul- taneously. Interestingly, the performance improvement is more pronounced when the source and assisting languages are closely related, e.g., French and Spanish. 1 Introduction The central problem of Information Retrieval (IR) is to satisfy the user’s information need, which is typically expressed through a short (typically 2-3 words) and often ambiguous query. The problem of matching the user’s query to the documents is rendered difficult by natural language phenomena like morphological variations, polysemy and syn- onymy. Relevance Feedback (RF) tries to over- come these problems by eliciting user feedback on the relevance of documents obtained from the initial ranking and then uses it to automatically refine the query. Since user input is hard to ob- tain, Pseudo-Relevance Feedback (PRF) (Buckley et al., 1994; Xu and Croft, 2000; Mitra et al., 1998) is used as an alternative, wherein RF is performed by assuming the top k documents from the initial retrieval as being relevant to the query. Based on the above assumption, the terms in the feedback document set are analyzed to choose the most dis- tinguishing set of terms that characterize the feed- back documents and as a result the relevance of a document. Query refinement is done by adding the terms obtained through PRF, along with their weights, to the actual query. Although PRF has been shown to improve re- trieval, it suffers from the following drawbacks: (a) the type of term associations obtained for query expansion is restricted to co-occurrence based re- lationships in the feedback documents, and thus other types of term associations such as lexical and semantic relations (morphological variants, syn- onyms) are not explicitly captured, and (b) due to the inherent …

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