Natural language processing involves several different techniques for human language interpretation, ranging from statistical and machine learning methods to algorithmic and rules-based approaches. A wide range of approaches are necessity because text-and voice-based data, like practical applications, varies widely.
New York, Jan. 14, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Natural Language Processing Market (2019-2025)" - https://www.reportlinker.com/p05838704/?utm_source=GNW
Basic NLP tasks include tokenization and sorting, lemmatization/stemming, speech tagging, language detection, and semantic relationship identification. NLP tasks generally break down language into smaller, simpler pieces, understand the relationships between the pieces, and explore how the pieces work together to create meaning.
Growing demand for enhanced customer experience, increased use of smart devices, increased choice in application areas are expected to drive growth in the natural language processing market. Moreover, growing investments in the healthcare industry, increasing deployment of cloud-based and web business applications with rising machine-to-machine technology are further fueling the growth of the demand for natural language processing. Natural language processing demand is anticipated to witness an increase in professional services around the globe with growing need. Being an easily deployable and cost-effective cloud platform, natural language processing makes it more suitable for professional services.
Based on Component, the market is segmented into Solution and Services. Based on Application, the market is segmented into Text Classification, Machine Translation, Question Answering, Sentiment Analysis, Information Extraction, Automatic Summarization and Others. The sentiment analysis application is one of NLP’s most common tools, with a large number of courses, tutorials, and applications focused on analyzing feelings from different datasets, ranging from corporate surveys to movie reviews. It is the automated process that uses AI to classify positive, negative, and text-neutral opinions. Analysis of sentiment is widely used to gain insights from comments on social media, survey responses, and product reviews, and to make decisions based on data. Based on type, the market is segmented into Rule Based, Statistical and Hybrid. Based on Deployment Type, the market is segmented into On-premise and Cloud. Based on Industry Vertical, the market is segmented into BFSI, IT & Telecom, Healthcare, Retail & eCommerce, Government & Defense, Media & Entertainment, Manufacturing and Others.
Based on Regions, the market is segmented into North America, Europe, Asia Pacific, and Latin America, Middle East & Africa. North America is projected to have the largest market size during the forecast period, while the region is witnessing rapid technological advances; thus, the high adoption of digital technologies is one of the drivers of NLP market growth in the region. In addition, the region, a well-established economy, has seen large-scale investment in AI-enabled infrastructure, as a result of which both start-ups and well-established companies are more focused on developing creative NLP-enabled solutions for diverse verticals.
The major strategies followed by the market participants are Product launches and Partnerships & Collaborations. Based on the Analysis presented in the Cardinal matrix, Microsoft Corporation, Google, Inc., and Apple, Inc. are some of the forerunners in the Natural Language Processing Market. The market research report covers the analysis of key stake holders of the market. Key companies profiled in the report include IBM Corporation, Microsoft Corporation, Google, Inc., Amazon.com, Inc., Apple, Inc., Facebook, Inc., Intel Corporation, SAS Institute, Inc., Baidu, Inc., Health Fidelity, Inc., Conversica, Inc., and Inbenta Technologies, Inc.
Recent strategies deployed in Natural Language Processing Market
Partnerships, Collaborations, and Agreements:
Nov-2019: Microsoft signed partnership agreement with Graphcore in which Microsoft will develop systems for Azure and enhancing the advanced natural language processing and machine learning models on IPUs.
Apr-2019: SAS partnered with Citi and EY for the NextGen Project that uses artificial intelligence (AI) in order to develop a risk analytics scoring engine. This project is aimed at analysing the global trade transactions in depth and uses advanced analytics and natural language processing for understanding the networks of related parties, customer activity, and unstructured data in a better way.
Apr-2019: Google collaborated with Grid Dynamics, a provider of engineering and consulting services. This collaboration is centered on the acceleration of enterprise adoption of Google Cloud Platform (GCP) Technologies. Grid will use the GCP API for accelerating the development of machine learning models for supply chain optimization, predictive analytics, computer vision, conversational commerce, deep learning, and natural language processing.
Mar-2019: SAS Institute came into partnership with NVIDIA for helping the businesses in bringing the artificial intelligence (AI) into their organizations. The companies are coming together across computer vision, machine learning, and natural language processing, with NVIDIA GPUs and CUDA-X AI acceleration libraries.
Jan-2019: Health Fidelity collaborated with Change Healthcare for using risk adjustment solution of Change Healthcare. Its risk adjustment solution has been embedded with machine learning technology and natural language processing (NLP) for helping the ACA Commercial, Medicare Advantage, and Medicaid Payers in increasing claim accuracy and better addressing the compliance obligations.
Acquisition and Mergers:
Jul-2018: Facebook acquired Bloomsbury AI, a UK based startup whose natural language AI owned the considerable value. The acquisition strengthens the natural language processing research and helps the company in further understanding the natural language and its applications.
May-2018: Microsoft announced the acquisition of Semantic Machines, developer of new approaches for building conversational AI. Together the companies will develop their work in conversational AI with Microsoft’s digital assistant Cortana and social chatbots like XiaoIce.
Jan-2018: Conversica acquired Intelligens.ai, a provider of conversational AI for marketing and sales focused on Latin America market. The acquisition adds Intelligens technology, data set, and conversations to the Conversica’s conversational AI platform, allowing customers to take the advantages of new capabilities and increased AI accuracy.
Jul-2017: Baidu took over KITT.AI, a developer of technologies that enables voice interaction with digital devices and services. The acquisition bolsters Baidu’s AI technology.
Product Launches and Product Expansions:
Nov-2019: Baidu introduced PaddlePaddle upgrades that include a streamlined toolkit dubbed Paddle Lite 2.0. It is the updated version of Baidu’s natural language processing framework.
Nov-2019: Microsoft Research’s Natural Language Processing Group unveiled dialogue generative pre trained transformer. DialoGPT is a deep-learning natural processing model for use in automatic conversation response generation. This model has been trained on more than 147M dialogues and achieves the results on several benchmarks.
Oct-2019: Google introduced two new dialog datasets for natural language processing development. These datasets are: Taskmaster-1 and Coached Conversational Preference Elicitation (CCPE). The datasets includes thousands of conversations as well as annotations and labels for training the digital assistants for better determining the intentions and preferences of users.
Aug-2019: Conversica made advancements in its conversational AI platform that powers the millions of front-office conversations between Conversica’s AI assistants and people. The new enhancements offer the business users the flexibility and freedom for personalizing the conversations. The features include next generation of conversation editor, new users profile capability, and expanded library of conversation types & third-party integrations.
Aug-2019: Facebook announced the launch of Misspelling Oblivious (word) Embeddings (MOE), a new model for bolstering the use of natural language processing. This model is a mixture of a supervised task and fastText that is embedded with misspellings close to their correct variants.
Jul-2019: Baidu released ERNIE 2.0, a conversational AI framework and model that works in Chinese and English. ERNIE 2.0, a Chinese language understanding model, which outperformed other high-ranking NLP models.
Scope of the Study
• Text Classification
• Machine Translation
• Question Answering
• Sentiment Analysis
• Information Extraction
• Automatic Summarization
• Rule Based
By Deployment Type
By Industry Vertical
• IT & Telecom
• Retail & eCommerce
• Government & Defense
• Media & Entertainment
• North America
o Rest of North America
o Rest of Europe
• Asia Pacific
o South Korea
o Rest of Asia Pacific
o Saudi Arabia
o South Africa
o Rest of LAMEA
• IBM Corporation
• Microsoft Corporation
• Google, Inc.
• Amazon.com, Inc.
• Apple, Inc.
• Facebook, Inc.
• Intel Corporation
• SAS Institute, Inc.
• Baidu, Inc.
• Health Fidelity, Inc.
• Conversica, Inc.
• Inbenta Technologies, Inc.
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