Recent Trends in Deep Learning for Natural Language Processing and Scope for Asian Languages

Abstract

Natural language processing (NLP) studies the techniques and procedures that allow a machine to converse using human language. Recent advances in artificial intelligence and communication technology have considerably enhanced natural language processing applications. Due to improvements in deep learning, virtually every aspect of artificial intelligence, including natural language processing, has made substantial progress. Deep learning methods use many layers of neurons to construct a neural network. Recurrent Neural Networks and their variants like long short term (LSTM), bidirectional LSTM, are some of the most popular deep learning techniques. This article reviews deep learning techniques employed in NLP- specific approaches in the last few years. We also studied the issues faced by researchers while trying to apply deep learning to Asian languages. We also highlight some critical deep learning research work carried out recently for Indian and other Asian languages. In addition, we discuss fundamental linguistic processing challenges and suggest future scopes for the topic.