Context based NLP framework of textual tagging for low resource language

Understanding the context of any phrase or extracting relationships requires part of speech tagging (POS). This article proposes an RNN-based POS tagger and compares its performance with some of the existing POS tagging methods. We present novel LSTM-based RNN architecture for POS tagging. The study attempts to determine the usefulness of machine learning and deep learning techniques for tagging part-of-speech of words for the low-resource Hindi language, which is an Indo-Aryan language spoken mostly in India. During the experiments, different deep learning architecture (ANN and RNN) and machine learning methods (HMM, SVM, DT) have been used. A multi-representational treebank and an open-source dataset have been used for the performance analysis of the proposed framework. The experimental results in terms of macro-measured variables have shown better results compared to some state-of-the-art methods.