Abstract
Our first objective in participating in FIRE evaluation campaigns is to analyze the retrieval effectiveness of various indexing and search strategies when dealing with corpora written in Hindi, Bengali and Marathi languages. As a second goal, we have developed new and more aggressive stemming strategies for both Marathi and Hindi languages during this second campaign. We have compared their retrieval effectiveness with both light stemming strategy and n-gram language-independent approach. As another language-independent indexing strategy, we have evaluated the trunc-n method in which the indexing term is formed by considering only the first n letters of each word. To evaluate these solutions we have used various IR models including models derived from Divergence from Randomness (DFR), Language Model (LM) as well as Okapi, or the classical tf idf vector-processing approach.
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