Role of Intelligent Techniques in Natural Language Processing: An Empirical Study

Nowadays intelligent computing with natural language processing (NLP) is growing rapidly world wide. This paper describes the use of intelligent computing strategies in the field of NLP. Various intelligent techniques such as Artificial Neural Network, Support Vector Machine, Conditional Random Field, Hidden Markov Assumption, and Bayes’ Rule and with some Stochastic Process are used in sub fields of NLP such as Machine Translation, Sentiment Analysis, Information Retrieval, Information Extraction, Question Answering and Named Entity Recognition are used in to develop a reliable and efficient system with high accuracy. In this application area, both training and testing datasets with authorized corpus are required to test the proposed model. Most of the exact experimental values are recorded to handle the real-time situation for further study.