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(ne)C2, tf idf and Okapi) were used to evaluate the influence of these stemmers as well as n-grams and trunc-n 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(n e)C2, DFR-PL2 and Okapi IR models result the best performance. For this language trunc-n 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.
CITATION STYLE
Akasereh, M., & Savoy, J. (2013). Ad Hoc retrieval with Marathi language. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7536 LNCS, pp. 23–37). https://doi.org/10.1007/978-3-642-40087-2_3
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