Natural Language Processing Market: Overview
Natural language processing (NLP) is a computer application under artificial intelligence that can understand human language. This computerized technique allows human communication to be analyzed and interpreted by the computer on the basis of a set of technologies and theories. The goal of natural language processing is to minimize the time taken to understand computer languages such as Ruby, C, C++, and Java. NLP finds application in the analysis of big data owing to the fact that massive amounts of data are being generated in current business scenarios from sources such as audio, emails, web blogs, documents, social networking sites, and forums. Reports indicate that the global natural language processing market will prove to be extremely lucrative for the future.
Some of the leading competitors in the global natural language processing market are:
Microsoft Corporation, SAS Institute Inc., Hewlett-Packard, Dolbey Systems, Google, Netbase Solutions, IBM Corporation, 3M Company, Verint Systems, and Apple Inc.
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The major types of natural language processing solutions include statistical NLP, rule-based NLP, and hybrid NLP. Natural language processing includes several recognition, analytics, and operational technologies such as optical character recognition (OCR), auto coding, text analytics, interactive voice response (IVR), pattern and image recognition, classification and categorization, and speech analytics. Natural language processing can be a cloud-based or on-premise model and is used for applications such as information extraction, question answering, machine translation, and report generation in a number of industry verticals, including automotive, retail and consumer goods, high tech and electronics, government, banking, financial services, and insurance (BFSI), health care and life sciences, research and education, and media and entertainment.
The research report offers readers a clear and objective outlook of the global natural language processing market and highlights the key driving and restraining factors along with the opportunities and threats that lie ahead. The various market dynamics have been carefully analyzed to give clients a clear idea on the strategic moves of their rivals. The structure and scope of the natural language processing market have been explored and projections based on past and current trends have been provided.
This study by TMR is all-encompassing framework of the dynamics of the market. It mainly comprises critical assessment of consumers’ or customers’ journeys, current and emerging avenues, and strategic framework to enable CXOs take effective decisions.
Our key underpinning is the 4-Quadrant Framework EIRS that offers detailed visualization of four elements:
- Customer Experience Maps
- Insights and Tools based on data-driven research
- Actionable Results to meet all the business priorities
- Strategic Frameworks to boost the growth journey
The study strives to evaluate the current and future growth prospects, untapped avenues, factors shaping their revenue potential, and demand and consumption patterns in the global market by breaking it into region-wise assessment.
The following regional segments are covered comprehensively:
- North America
- Asia Pacific
- Latin America
- The Middle East and Africa
The global market for natural language processing is significantly driven by the massive jump in digital data, the rise in the utility of smart devices, and the increasing demand for enhanced customer experience. In addition to this, the surging investments in the healthcare sector and the emergence of several new application areas are anticipated to further boost the natural language processing market. The market, however, is faced by a number of challenges that players need to overcome in order to survive and compete. The main hurdles include the need for effective predictive technologies, the need to narrow the gap between machine and man, the implementation of strict government regulations, and fluctuating customer adoption trends.
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Recognition technologies are the most widely used technologies in NLP. Most companies, from large enterprises to small-scale organizations, use some form of recognition technology such as optical character recognition, interactive voice recognition, or image recognition. Natural language processing is most commonly used for information extraction and machine translation. IT and telecom, automotive, and defense and aerospace are the leading industries that use NLP for various applications.
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