Multilingual PRF
Manoj Kumar Chinnakotla, Karthik Raman, Pushpak Bhattacharyya · 2010
In this paper, we present a novel approach to Pseudo-Relevance Feedback (PRF) called Multilingual PRF (MultiPRF). The key idea is to harness multilinguality. Given a query in a language, we take the help of another language to ameliorate the well known problems of PRF, viz. (a) The expansion terms from PRF are primarily based on co-occurrence relationships with query terms, and thus other terms which are lexically and semantically related, such as morphological variants and synonyms, are not explicitly captured, and (b) PRF is quite sensitive to the quality of the initially retrieved top k documents and is thus not robust. In MultiPRF, given a query in language L1, it is translated into language L2 and PRF is performed on a collection in language L2 and the resultant feedback model is translated from L2 back into L1. The final feedback model is obtained by combining the translated model with the original feedback model of the query in L1.