Developing Hindi POS Tagger for Homoeopathy Clinical Language

Pramod Sukhadeve; Sanjay Dwivedi

Advances in Computer Science and Information Technology. Computer Science and Information Technology. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III

Research Article

Developing Hindi POS Tagger for Homoeopathy Clinical Language

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@INPROCEEDINGS{10.1007/978-3-642-27317-9_32,
    author={Pramod Sukhadeve and Sanjay Dwivedi},
    title={Developing Hindi POS Tagger for Homoeopathy Clinical Language},
    proceedings={Advances in Computer Science and Information Technology. Computer Science and Information Technology. Second International Conference, CCSIT 2012, Bangalore, India, January 2-4, 2012. Proceedings, Part III},
    proceedings_a={CCSIT PART  III},
    year={2012},
    month={11},
    keywords={POS tagging Grammar rules Homoeopathic Corpus clinical words POS Approaches},
    doi={10.1007/978-3-642-27317-9_32}
}

Pramod Sukhadeve
Sanjay Dwivedi
Year: 2012
Developing Hindi POS Tagger for Homoeopathy Clinical Language
CCSIT PART III
Springer
DOI: 10.1007/978-3-642-27317-9_32

Pramod Sukhadeve¹^,*, Sanjay Dwivedi¹^,*

1: Babasaheb Bhimrao Ambedkar University

*Contact email: sukhadeve.pramod@gmail.com, skd200@yahoo.com

Abstract

Part of speech tagging is one of the most basic preprocessing tasks of machine translation in NLP. The problem of tagging in natural language processing is to find a way to tag every word in a text as a meticulous part of speech. In this paper, we first present different approaches and some of the grammatical rules for tagging homoeopathy clinical sentences. Further in the paper we have our approach development of a Hindi tagger by using homoeopathy clinical sentences, for this purpose we have developed a corpus comprising of 250 sentences at present having 20060 words and 3420 tokens. The accuracy of POS tagging is calculated by using standard formula, and achieved the accuracy of 89.55%.

Keywords: POS tagging Grammar rules Homoeopathic Corpus clinical words POS Approaches

Published: 2012-11-09

: http://dx.doi.org/10.1007/978-3-642-27317-9_32