Global Market Research Report on Natural Language Processing (NLP) Market 2016 is a professional and in-depth complete study on the current state of the Natural Language Processing (NLP) worldwide.

Deerfield Beach, FL -- (SBWire) -- 05/18/2017 --The greatest challenge for Natural Language Processing applications is to identify human voice with different patterns, tones, pronunciations and to convert it into a programmable language. The main goal of such applications is to reduce the use of specialized languages such as Java, Ruby, C, etc., and introduce only human language in all computerized systems. The biggest advantage of this application is its basic and advanced level of interaction with humans. In future, humans would need to feed codes to systems orally instead of writing them on the computer system. Currently, such applications are used in voice recognition systems; however, eventually, these applications would find substantial demand in varied domains. Convenient interaction with machines would drive the global NLP market. Additionally, customer care centers are continuously adopting natural language processing technologies to provide better customer experience that contributes in driving the global market.

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The technologies used in NLP are Recognition, Operational and Analytics technologies. Currently, Recognition technologies are widely used from small scale to large enterprises. A recognition technology comprises of interactive voice recognition (IVR), optical character recognition and image recognition amongst, which IVR is most commonly used in various applications. Various voice recognition systems such as automatic speed control systems, temperature controllers, etc. are also developed using recognition technology. The capability of NLP to operate the system only when the recorded voice is detected in different applications is contributing to the revenue stream of the technology segment.The end users of Natural Language Processing are Machine translation, Information extraction, Report generation, Question answering, and others. Machine translation converts the source language to target language, while information extraction automatically extracts the information in a structured format. Use of such applications makes the conversion from human language to machine language easy.

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MARKET BY TYPES

Statistical NLP
Hybrid based NLP
Rule NLP
MARKET BY TECHNOLOGY

Recognition technologies
Operational technologies
Analytics technologies
MARKET BY APPLICATIONS

Machine translation
Information extraction
Report generation
Question answering
Others
MARKET BY END USER

Automotive
Healthcare
Banking Financial Services and Insurance
IT and Telecom
Government, Defense & Aerospace
Media & Advertisement
Academia and Education
MARKET BY GEOGRAPHY

North America
Europe
Asia-Pacific
LAMEA
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