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dc.contributor.authorHasan, Fahim Muhammad
dc.contributor.authorNaushad UzZaman
dc.contributor.authorKhan, Mumit
dc.date.accessioned2010-10-21T09:29:19Z
dc.date.available2010-10-21T09:29:19Z
dc.date.copyright2006
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/10361/625
dc.descriptionIncludes bibliographical references (page 6-7).
dc.description.abstractThere are different approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. In this paper we compare the performance of a few POS tagging techniques for Bangla language, e.g. statistical approach (n-gram, HMM) and transformation based approach (Brill’s tagger). A supervised POS tagging approach requires a large amount of annotated training corpus to tag properly. At this initial stage of POS-tagging for Bangla, we have very limited resource of annotated corpus. We tried to see which technique maximizes the performance with this limited resource. We also checked the performance for English and tried to conclude how these techniques might perform if we can manage a substantial amount of annotated corpus.en_US
dc.description.statementofresponsibilityNaushad UzZaman
dc.description.statementofresponsibilityFahim Muhammad Hasan
dc.description.statementofresponsibilityMumit Khan
dc.format.extent7 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.titleComparion of different POS tagging technique (N-Gram, HMM and Brill's tagger) for Banglaen_US
dc.typeArticleen_US
dc.contributor.departmentCenter for Research on Bangla Language Processing (CRBLP), BRAC University


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