ABSTRACT
Understanding and processing human language has always been a complicated task in computational theory. Natural language processing, a combination of artificial intelligence and computational linguistics, employs computational techniques to understand the structure of human language. The challenges in the field are increasing day by day with the explosive growth of text contents on the Internet, varying text forms used in social media, and handling conversational complexities associated with intelligent devices. Natural language processing ranges from analyzing natural language by tokenizing and parsing techniques to resolving ambiguities and co-references in language. This chapter explains the basics of natural language processing that includes text processing techniques, parsing, semantic analysis, and the latest trend in deep learning models, which promises excellent improvement in natural language processing tasks, with simple examples that give more comprehension about the concepts in brief. The chapter also includes a comparative study of long short-term memory and gated recurrent unit for the sequence to sequence modeling with step-by-step implementation details of opinion summarization.
