By Sauparna Palchowdhury, Prasenjit Majumder, Dipasree Pal, Ayan Bandyopadhyay (auth.), Prasenjit Majumder, Mandar Mitra, Pushpak Bhattacharyya, L. Venkata Subramaniam, Danish Contractor, Paolo Rosso (eds.)
This publication constitutes the completely refereed post-proceedings of the second one and 3rd Workshops of the discussion board for info Retrieval overview, fireplace 2010 and 2011, on Multi-lingual details entry in South Asian Languages held in Gandhinagar, India, in February 2010 and in Bombay, India, in December 2011. the amount brings jointly revised and improved types of a complete of 29 papers. The papers are geared up in topical sections on a number of features of multi-lingual details access.
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Additional resources for Multilingual Information Access in South Asian Languages: Second International Workshop, FIRE 2010, Gandhinagar, India, February 19-21, 2010 and Third International Workshop, FIRE 2011, Bombay, India, December 2-4, 2011, Revised Selected Papers
In: 2006 ACM SIGIR Conference on OSIR (2006) 13. : Phrasal Translation and Query Expansion Techniques for Cross-Language Information Retrieval. ACM SIGIR Forum (1997) 14. : Summarization-based Query Expansion in Information Retrieval. In: COLING 1998 Proceedings of the 17th International Conference on Computational Linguistics, vol. 2 (1998) 15. : Improving Pseudo-Relevance Feedback in Web Information Retrieval Using Web Page Segmentation. ch Abstract. 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.
5 · log 7 · 1 is defined as follows while In the DFR-GL2 model Equation 5. 1 1 · 1 is defined as in 8 1 Finally we have also implemented one approach based on language model (LM) . In this case, the underlying paradigm is based on a non-parametric probabilistic model. We have opted the model suggested by Hiemstra  using the JelinekMercer smoothing  scheme as shown in Equation 9. 30 for all index terms) lc is an estimation of the corpus C length. Stopword List Different words do not have the same importance in describing the semantic content of a document (or a query).
Our evaluation was carried out with title and description ﬁelds of test topics, and the Lemur search engine. We used plain unprocessed word index as the baseline, and n-gramming and stemming as competing methods. The evaluation results show 30%, 16% and 70% relative mean average precision improvements for Bengali, Gujarati and Marathi respectively when comparing the FCG method to plain words. The method shows competitive performance in comparison to n-gramming and stemming. 1 Introduction One of the basic problems of full-text retrieval is the variation of word forms that is caused by the morphology of natural languages.