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Low-density languages are also known as lesser-known, poorly-described, less-resourced, minority or less-computerized language because they have fewer resources available. Collecting and annotating a voluminous corpus for these languages prove to be quite daunting. For developing any NLP application for a low-density language, one needs to have an annotated corpus and a standard scheme for annotation. Because of their non-standard usage in text and other linguistic nuances, they pose significant challenges that are of linguistic and technical in nature. The present paper highlights some of the underlying issues and challenges in developing statistical POS taggers applying SVM and CRF++ for Sambalpuri, a less-resourced Eastern Indo-Aryan language. A corpus of approximately 121k is collected from the web and converted into Unicode encoding. The whole corpus is annotated under the BIS (Bureau of Indian Standards) annotation scheme devised for Odia under the ILCI (Indian Languages Corpora Initiative) Corpora Project. Both the taggers are trained and tested with approximately 80k and 13k respectively. The SVM tagger provides 83% accuracy while the CRF++ has 71.56% which is less in comparison to the former.

Added on March 22, 2016

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  • Contributed by : Atul Ojha
  • Product Type : Research Paper
  • License Type : Freeware
  • System Requirement : Not Applicable
  • Author : Pitambar Behera, Atul Kr. Ojha, Girish Nath Jha
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