Development of Part of Speech Tagger Using Deep Learning
Aarti Singh1, Charu Verma2, Swagata Seal3, Varsha Singh4
1Aarti Singh*, Department of Computer Science, Banasthali Vidyapith, India.
2Charu Verma, Department of Computer Science, Banasthali Vidyapith, India.
3Swagata Seal, Department of Computer Science, Banasthali Vidyapith, India.
4Varsha Singh, Department of Computer Science, Banasthali Vidyapith, India.
Manuscript received on September 10, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 3384-3391 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1531109119/2019©BEIESP | DOI: 10.35940/ijeat.A1531.109119
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Part of speech tagging is the initial step in development of NLP (natural language processing) application. POS Tagging is sequence labelling task in which we assign Part-of-speech to every word (Wi) which is sequence in sentence and tag (Ti) to corresponding word as label such as (Wi/Ti…. Wn/Tn). In this research project part of speech tagging is perform on Hindi. Hindi is the fourth most popular language and spoken by approximately 4billion people across the globe. Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. In this paper we have shown the development of POS tagging using neural approach.
Keywords: POS Tagging, LSTM, RNN, Hindi.