Exploiting Named Entity Mentions Towards Code Mixed IR : Working Notes for the UB system submission for MSIR@FIRE-2016

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. CCS Concepts •Information systems→Multilingual and cross-lingual retrieval; •Computing methodologies→Natural language processing;