Special issue on deep learning for natural language processing

Making a computer to understand, analyze and interpret the meaning for human communication language is Natural Language Processing (NLP). Language in general has been defined as the means of communication between two or more individuals to share their ideologies and feelings. Traditionally, the communication between human is realized throughvarious natural languages.However,when this is extended to human computer interaction, the scenario changes distinctly with human communicating in their own natural language and this communication should be interpreted such that the computer understands the appropriate meaning communicated through the natural language with an eye on not losing some essential properties such as emotions, feelings and context. This aspect of training the computer system to understand and learn natural languages has made NLP an interesting, challenging and demanding research area especially when it comes to artificial intelligence and human computer interaction. Algorithms of NLP are basically derived from machine learning approaches, where it uses the machine learning approaches to learn the rules automatically for analyzing large volume of data. A fast-automatic processing for natural languages is very impossible by machine learning approaches. Hence, it is very necessary to find a more advanced approach to replace machine learning for providing fast-automatic NLP in various real time applications. Deep learning is one of the more advanced machine learning approaches that extends the features of artificial neural networks.Deep learning can extract and classify features automatically and fast. The primary objective of deep learning is to classify and analyze the different patterns generated out of natural languages. Deep learning provides a multi-layer abstraction approach towards non-linear feature and pattern