ISM@FIRE-2015: Mixed Script Information Retrieval

This paper describes the approach we have used for identification of languages for a set of terms written in Roman script and approaches for the retrieval in mixed script domain, in FIRE-2015. The first approach identifies the class (native language of terms and whether a term is any named entity or of any other type) of given terms/words. MaxEnt a supervised classifier has been used for the classification which performed best for strict f-measure NE has score is 0.46 and strict f-measure NE_P has score 0.24. For the MSIR subtask Divergence from Randomness (DFR) based approach is used and performed better with block indexing and query formulation. Overall scores of our submission on NDCG@10 0.4335, 0.5328, 0.4489 and 0.5369 for ISMD1, ISMD2, ISMD3 and ISMD4 respectively. .