A TENGRAM method based part-of-speech tagging of multi-category words in Hindi language |
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| Authors: | J.P. Gupta Devendra K. Tayal Arti Gupta |
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| Affiliation: | 1. Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India;2. Department of Computer Engineering, GGSIP University, New Delhi, India;3. Department of Computer Science & IT, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India;1. St. Petersburg State University, Laboratory of Geochronology, 199094 St. Petersburg, Russia;2. Moscow State University, Faculty of Geography, 119992 Moscow, Russia;3. Institute of Geography, Russian Academy of Sciences, 119017 Moscow, Russia;1. Blekinge Institute of Technology, Karlskrona, Sweden;2. Chalmers and the University of Gothenburg, Gothenburg, Sweden;1. Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin 300350, China;2. Japan Advanced Institute of Science and Technology, Japan;1. Department of Global Health, University of Washington, Seattle, WA, United States;2. School of Nursing, Johns Hopkins University, Baltimore, MD, United States;3. School of Public Health, Sun Yat-Sen University, Guangzhou, China;4. School of Nursing, Fudan University, Shanghai, China;5. School of Nursing, Peking Union Medical College, Beijing, China;6. School of Nursing, University of Washington, Seattle, WA, United States |
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| Abstract: | In this paper, we have dealt on the problem of part-of-speech tagging of multi-category words which appear within the sentences of Hindi language. Firstly, a Hindi tagger is proposed which provides part-of-speech tags developed using grammar of Hindi language. For this purpose, Hindi Devanagari alphabets are used and their Hindi transliteration is done within the proposed tagger. Thereafter, a Rules’ based TENGRAM method is described with an illustrative example, which guides to disambiguate multi-category words within sentences of Hindi corpus. The rules generated in TENGRAM are the result of computation of discernibility matrices, discernibility functions and reducts. These computations have been generated from decision tables which are based on theory of Rough sets. Basically, a discernibility matrix helps in cutting down indiscernible condition attributes; a discernibility function has rows corresponding to each column in the discernibility matrix which develops reducts; and the reducts provide a minimal subset of attributes which preserve indiscernibility relation of decision tables and hence they generate the decision rules. |
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