Natural Language Processing

Natural language processing is a field of artificial intelligence that deals with reducing the gap between humans and machines. It tries to bridge the gap between human and machine language. There are many different applications in natural language processing some of which include automatic translation, speech understanding, information extraction, question answering, text generation etc. It has proved to be helpful in applications such as optical character recognition systems and ASR systems. Theoretically NLP is “a principled propels range of computational methods for analyzing and presenting naturally getting texts at one or much level of lexical analysis for the design of getting like human language processing for a field of tasks or applications.”

The first computer-based application in relation to natural language processing was machine translation. Natural Language Processing has four types of general approaches. They are symbolic, statistical, connectionist and hybrid. While symbolic and statistical approaches have existed since the start, connectionist NLP started from the 1960’s. Statistical approaches transcended in the 1980’s. The tract of natural language processing (NLP) created nearby five decades ago with the machine translation systems. Warren Weaver and Andrew Donald Booth in 1946 discussed the technical feasibility of machine translation. These techniques were developed which were used in the breaking of enemy codes during the 2nd World

This kind of Question Answering System has some characteristics which become it distinct from other categories of Question Answering System especially open-domain Question Answering, which performance over a huge document collection, contain the WWW [11].

In closed-domain Question Answering, the database is entirely short and particular to a targeted area so whenever the question is fired accurate response may often be found in only very few documents; the system does not have a huge recovery set abundant of useful candidates for choice.

The close domain Question Answering System necessarily to response for all types of questions whether it is simple or composite in command to usage it as a question answering system for any association or organization. The system should report a complete answer, which can be extended and composite, because it has to, e.g., clarify the context of the problem determined in the question, explain the options of a service, give instructions or procedures,