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2006

Morphological Richness Offsets Resource Demand - Experiences in Constructing a POS Tagger for Hindi

8 years 11 months ago
Morphological Richness Offsets Resource Demand - Experiences in Constructing a POS Tagger for Hindi
In this paper we report our work on building a POS tagger for a morphologically rich language- Hindi. The theme of the research is to vindicate the stand that- if morphology is strong and harnessable, then lack of training corpora is not debilitating. We establish a methodology of POS tagging which the resource disadvantaged (lacking annotated corpora) languages can make use of. The methodology makes use of locally annotated modestly-sized corpora (15,562 words), exhaustive morpohological analysis backed by high-coverage lexicon and a decision tree based learning algorithm (CN2). The evaluation of the system was done with 4-fold cross validation of the corpora in the news domain (www.bbc.co.uk/hindi). The current accuracy of POS tagging is 93.45% and can be further improved. 1 Motivation and Problem Definition Part-Of-Speech (POS) tagging is a complex task fraught with challenges like ambiguity of parts of speech and handling of "lexical absence" (proper nouns, foreign words...
Smriti Singh, Kuhoo Gupta, Manish Shrivastava, Pus
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where ACL
Authors Smriti Singh, Kuhoo Gupta, Manish Shrivastava, Pushpak Bhattacharyya
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