Graph Based Semi-Supervised Learning Approach for Tamil POS tagging

Abstract

Parts of Speech (POS) tagging is an important pre-requisite for various Natural Language Processing tasks. POS tagging is rather challenging for morphologically rich languages such as Tamil. Being low-resourced, Tamil does not have a large POS annotated corpus to build good quality POS taggers using supervised machine learning techniques. In order to gain the maximum out of the existing Tamil POS tagged corpora, we have developed a graph-based semi-supervised learning approach to classify unlabelled data by exploiting a small sized POS labelled data set. In this approach, both labelled and unlabelled data are converted to vectors using word embeddings and a weighted graph is constructed using Mahalanobis distance. Then semi-supervised learning (SSL) algorithms are used to classify the unlabelled data. We were able to gain an accuracy of 0.8743 over an accuracy of 0.7333 produced by a CRF tagger for the same limited size corpus.

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