Exploiting named entity mentions towards code mixed IR: Working notes for the UB system submission for MSIR@FIRE'16

A sizable percentage of online user generated content is susceptible to code switching and code mixing owing to a variety of reasons. Thus, an expected consequence is that adhoc user queries on such data are also inherently code mixed. This paper thus presents our solution for a similar scenario: information retrieval on code mixed Hindi-English tweets. We explore techniques in information extraction, clustering and query expansion as part of this work and present our results on the test dataset. Our system achieved a MAP of 0.0217 on the test set and placed third on the rankings.