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.
For the three studied languages, our experiments tend to show that IR models derived from Divergence from Randomness (DFR) paradigm tend to produce the best overall results. For these languages, our various experiments demonstrate also that either an aggressive stemming procedure or the trunc-n indexing approach produces better retrieval effectiveness when compared to other word-based or n-gram language-independent approaches. Applying the Z-score as data fusion operator after a blind-query expansion tends also to improve the MAP of the merged run over the best single IR system.
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References
Savoy, J.: Combining Multiple Strategies for Effective Monolingual and Cross-Lingual Retrieval. IR Journal 7, 121–148 (2004)
Savoy, J.: Comparative Study of Monolingual and Multilingual Search Models for Use with Asian Languages. ACM - Transactions on Asian Languages Information Processing 4, 163–189 (2005)
Dolamic, L., Savoy, J.: UniNE at FIRE 2008: Hindi, Marathi and Bengali IR. FIRE 2008 Working Notes (2008)
Voorhees, E.M., Harman, D.K. (eds.): TREC. Experiment and Evaluation in Information Retrieval. The MIT Press, Cambridge (2005)
Robertson, S.E., Walker, S., Beaulieu, M.: Experimentation as a Way of Life: Okapi at TREC. Information Processing & Management 36, 95–108 (2002)
Amati, G., van Rijsbergen, C.J.: Probabilistic Models of Information Retrieval Based on Measuring the Divergence from Randomness. ACM Transactions on Information Systems 20, 357–389 (2002)
Hiemstra, D.: Using Language Models for Information Retrieval. Ph.D. Thesis (2000)
Hiemstra, D.: Term-Specific Smoothing for the Language Modeling Approach to Information Retrieval. In: Proceedings of ACM-SIGIR, pp. 35–41. The ACM Press (2002)
Zhai, C., Lafferty, J.: A Study of Smoothing Methods for Language Models Applied to Information Retrieval. ACM Transactions on Information Systems 22, 179–214 (2004)
Fox, C.: A Stop List for General Text. ACM-SIGIR Forum 24, 19–35 (1990)
Dolamic, L., Savoy, J.: When Stopword Lists Make the Difference. Journal of the American Society for Information Sciences and Technology 61, 200–203 (2010)
Savoy, J.: Light Stemming Approaches for the French, Portuguese, German and Hungarian Languages. In: Proceedings of ACM-SAC, pp. 1031–1035. The ACM Press (2006)
Harman, D.K.: How Effective is Suffxing? Journal of the American Society for Information Science 42, 7–15 (1991)
Porter, M.F.: An Algorithm for Suffix Stripping. Program 14, 130–137 (1980)
Fautsch, C., Savoy, J.: Algorithmic Stemmers or Morphological Analysis: An Evaluation. Journal of the American Society for Information Sciences and Technology 60, 1616–1624 (2009)
Buckley, C., Voorhees, E.M.: Retrieval System Evaluation. In: Voorhees, E.M., Harman, D.K. (eds.) TREC. Experiment and Evaluation in Information Retrieval, pp. 53–75. The MIT Press, Cambridge (2005)
Savoy, J.: Statistical Inference in Retrieval Effectiveness Evaluation. Information Processing & Management 33(4), 495-512
Abdou, S., Savoy, J.: Statistical and Comparative Evaluation of Various Indexing and Search Models. In: Ng, H.T., Leong, M.-K., Kan, M.-Y., Ji, D. (eds.) AIRS 2006. LNCS, vol. 4182, pp. 362–373. Springer, Heidelberg (2006)
McNamee, P., Mayfield, J.: Character N-gram Tokenization for European Language Text Retrieval. IR Journal 7, 73–97 (2004)
McNamee, P., Nicholas, C., Mayfield, J.: Addressing Morphological Variation in Alphabetic Languages. In: Proceedings of ACM-SIGIR 2009, pp. 75–82. The ACM Press (2009)
Buckley, C., Singhal, A., Mitra, M., Salton, G.: New Retrieval Approaches Using SMART. In: Proceedings of TREC-4, pp. 25–48. NIST Publication #500-236, Gaithersburg (1996)
Peat, H.J., Willett, P.: The Limitations of Term Co-Occurrence Data for Query Expansion in Document Retrieval Systems. Journal of the American Society for Information Science 42, 378–383 (1991)
Abdou, S., Savoy, J.: Searching in Medline: Stemming, Query Expansion, and Manual Indexing Evaluation. Information Processing & Management 44, 781–789 (2008)
Vogt, C.C., Cottrell, G.W.: Fusion via a Linear Combination of Scores. IR Journal 1, 151–173 (1999)
Fox, E.A., Shaw, J.A.: Combination of Multiple Searches. In: Proceedings of TREC-2, pp. 243–249. NIST Publication #500-215, Gaithersburg (1994)
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Savoy, J., Dolamic, L., Akasereh, M. (2013). Information Retrieval with Hindi, Bengali, and Marathi Languages: Evaluation and Analysis. In: Majumder, P., Mitra, M., Bhattacharyya, P., Subramaniam, L.V., Contractor, D., Rosso, P. (eds) Multilingual Information Access in South Asian Languages. Lecture Notes in Computer Science, vol 7536. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40087-2_30
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