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%.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Sukhadeve, P.P., Dwivedi, S.K. (2012). Developing Hindi POS Tagger for Homoeopathy Clinical Language. In: Meghanathan, N., Chaki, N., Nagamalai, D. (eds) Advances in Computer Science and Information Technology. Computer Science and Information Technology. CCSIT 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27317-9_32
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DOI: https://doi.org/10.1007/978-3-642-27317-9_32
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