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

TLDR: A deep learning based POS tagger for Nepali text is proposed which is built using Recurrent Neural Network, Long Short-Term Memory Networks, Gated Recurrent Unit and their bidirectional variants and shows significant improvement and outperforms the state-of-art POS taggers with more than 99% accuracy.

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.