Ad hoc retrieval with Marathi language

Our goal in participating in FIRE 2011 evaluation campaign is to analyse and evaluate the retrieval effectiveness of our implemented retrieval system when using Marathi language. We have developed a light and an aggressive stemmer for this language as well as a stopword list. In our experiment seven different IR models (language model, DFR-PL2, DFR-PB2, DFR-GL2, DFR-I(<i>n _e</i>)C2, <i>tf idf</i> and Okapi) were used to evaluate the influence of these stemmers as well as <i>n</i>-grams and trunc-<i>n</i> language-independent indexing strategies, on retrieval performance. We also applied a pseudo relevance-feedback or blind-query expansion approach to estimate the impact of this approach on enhancing the retrieval effectiveness. Our results show that for Marathi language DFR-I(<i>n _e</i>)C2, DFR-PL2 and Okapi IR models result the best performance. For this language trunc-<i>n</i> indexing strategy gives the best retrieval effectiveness comparing to other stemming and indexing approaches. Also the adopted pseudo-relevance feedback approach tends to enhance the retrieval effectiveness.