Pimpalshende A, Mahajan AR (2018) Extraction of root words using morphological analyzer for Hindi text. ![]() Pande BP, Tamta P, Dhami HS (2014) A Devanagari script based stemmer. Gupta V (2014) Hindi rule based stemmer for nouns. In: International conference on communication systems and network technologies, pp 554–558ĭubey P (2018) The Hindi to Dogri machine translation system: grammatical perspective. Mishra N, Mishra A (2011) Part of speech tagging for Hindi corpus. Int J Innov Technol Expl Eng (IJITEE) 8(8S3):114–120īagul P, Mishra A, Mahajan P, Kulkarni M, Dhopavkar G (2014) Rule based POS tagger for Marathi text. COLING (Demos) 2:163–174ĭutta S, Arora B (2019) Preprocessing for parts of speech (POS) tagging in Dogri language. Garg N, Goyal V, Preet S (2012) Rule based Hindi part of speech tagger. J Multimed Inf Syst 5(3):147–154Īntony PJ, Soman KP (2011) Parts of speech tagging for Indian languages: a literature survey. Modi D, Nain N, Nehra M (2018) Part-of-speech tagging for Hindi corpus in poor resource scenario. Modi D, Nain N (2016) Part-of-speech tagging of Hindi corpus using rule-based method. Kumawat D, Jain V (2015) POS tagging approaches: a comparison. In: Proceedings of the 2013 international conference on advances in computing, communications and informatics, ICACCI 2013, 2013, pp 1554–1559 Singh J, Joshi N, Mathur I (2013) Development of Marathi part of speech tagger using statistical approach. Kumar S (2018) Developing POS tagset for Dogri. Keywordsīhatta S, Parmara K, Patelb M (2015) Sanskrit tag-sets and part-of-speech tagging methods-a survey. ![]() Thereby Brill avoids most of the limitations of traditional rule-based taggers in his. Brill (1992) presented a rule-based PoS tagger which automatically infers rules from a training corpus based on transformation-based error-driven learning. ![]() The proposed system is evaluated over a number of corpus with six different parts of speech tags for Dogri, and hence, the evaluation is done on five datasets of Dogri corpus, and the corresponding results are also demonstrated in the paper. The limitations of the rule-based taggers are that they are non-automatic, costly and time-consuming. In this paper, a rule-based parts of speech tagger for Dogri (regional language of Jammu) language along with the algorithm and the modular structure of the system is presented. The methods of assigning tags to words are categorized into rule-based, stochastic and hybrid approach. As per English grammar, there are many parts of speech tags such as noun, adjective, pronoun, verb, adverb, preposition, conjunction and interjection. Parts of speech tagging is basically the problem of assigning the parts of speech tags to the words in the text. Parts of speech tagging is an important activity of natural language processing, information extraction, language translation, speech synthesis, question understanding and many more.
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