Evaluating Stemmers and Retrieval Fusion Approaches for Hindi: UNT at FIRE 2010

This paper describes the experiments conducted by the University of North Texas team as part of our participation in the Forum for Information Retrieval (FIRE). We concentrated on comparing the results using two morphological stemmers (YASS and Morfessor), studying the effect of using a part of speech tagger (Combined Random Fields) to weight the contribution of words with noun phrases, and to use a data fusion approach to improve performance of the system by combining these methods. We conducted our study using Hindi and explore the cross-language retrieval performance from English to Hindi using Google translations. Our results show that using the YASS stemmer yields a small increase in retrieval performance. Fusion of results also showed to be effective and improved results 5% in our experiments.