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 info entry in South Asian Languages held in Gandhinagar, India, in February 2010 and in Bombay, India, in December 2011. the quantity brings jointly revised and improved types of a complete of 29 papers. The papers are geared up in topical sections on quite a few points of multi-lingual info access.
Read Online or Download 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 PDF
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Additional info 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
It provides, however, a simple and usually easily implementable competitive alternative for lemmatization for languages that might lack language technology tools for information retrieval. g 5K words) once and they can be reused for the diﬀerent corpora of the same language without further processing. In this paper we present results of Frequent Case Generation on three Indian languages that are morphologically at least moderately complex: Bengali, Gujarati and Marathi. We compare the FCG method to two standard word form variation management methods, n-gramming  and stemming, our stemmer being GRAS .
We next identify pivot nodes - words that are connected by edges to a large number of other words. e. if the words that it is related to are also related to the pivot. Once such word classes are formed, stemming is done by mapping all the words in a class to the pivot for that class. 5 Results We present our evaluation results under ﬁve metrics, namely, MAP (mean average precision), GMAP, relevant returned (Rel-ret), p@10 (precision at 10) and R-precision (average of precison at R, R is the number of relevant document for a query).
8 1 Recall Fig. 3. 3 Gujarati Results The retrieval results of Gujarati are shown in Table 4. FCGs improved the performance about 11-16% when compared to the baseline and about 21-23% when compared to n-gramming. The GRAS stemmer was also slightly outperformed by FCG-70 and FCG-80. Precision at 10 and R-Prec improved also slightly with all the FCGs. The best FCG outperformed n-gramming and GRAS with MAP, GMAP and R-Prec slightly, but GRAS got the best P@10. In this case n-gram performs worse that plain word baseline in terms of precision at 10.
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 by Sauparna Palchowdhury, Prasenjit Majumder, Dipasree Pal, Ayan Bandyopadhyay (auth.), Prasenjit Majumder, Mandar Mitra, Pushpak Bhattacharyya, L. Venkata Subramaniam, Danish Contractor, Paolo Rosso (eds.)