Deep Learning Model for Tamil Part-of-Speech Tagging

Abstract

Part-of-Speech (POS) tagging is one of the popular Natural Language Processing (NLP) tasks. It is also considered to be a preliminary task for other NLP applications such as speech recognition, machine translation, and sentiment analysis. A few works have been published on POS tagging for the Tamil language. However, the performance of the POS tagger with unknown words is not explored in the literature. The appearance of unknown words is a frequently occurring problem in POS tagging and makes it a challenging task. In this paper, we propose a deep learning-based POS tagger for Tamil using Bi-directional Long Short Term Memory (BLSTM). The performance of the POS tagger was evaluated using known and unknown words. The POS tagger with regular word-level embeddings produces 99.83 and 92.46% accuracies for all known and 63.21% unknown words. It clearly shows that the accuracy decreases when the number of unknown words increases. To improve the performance of the POS tagger with unknown words, the proposed BLSTM model that uses word-level, character-level and pre-trained word embeddings. Test results of this model show a 2.57% improvement for 63.21% of unknown words, with an accuracy of 95.03%.