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A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language

Publication Type : Conference Paper

Publisher : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India .

Source : 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India (2018)

Url : https://ieeexplore.ieee.org/abstract/document/8554812

Campus : Coimbatore

School : School of Artificial Intelligence, School of Artificial Intelligence - Coimbatore, School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2018

Abstract : Part of Speech (POS) tagging is the most fundamental task in various natural language processing(NLP) applications such as speech recognition, information extraction and retrieval and so on. POS tagging involves annotation of appropriate tag for each token in the corpus based on its context and the syntax of the language. In computational linguistics, optimal POS tagger is of paramount importance since tagging errors can critically affect the performance of the complex NLP systems. Developing an efficient POS tagger for morphologically rich languages like Nepali is a challenging task. In this paper, a deep learning based POS tagger for Nepali text is proposed which is built using Recurrent Neural Network (RNN), Long Short-Term Memory Networks (LSTM), Gated Recurrent Unit (GRU) and their bidirectional variants. Performance metrics such as accuracy, precision, recall and F1-score were chosen for the model evaluation. It is observed from the results that our model shows significant improvement and outperforms the state-of-art POS taggers with more than 99% accuracy.

Cite this Research Publication : Prabha, G., Jyothsna, P.V., Shahina, K.K., Premjith, B., Soman, K.P., "A Deep Learning Approach for Part-of-Speech Tagging in Nepali Language," (2018) 2018 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2018, art. no. 8554812, pp. 1132-1136., DOI: 10.1109/ICACCI.2018.8554812

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