Cloud Natural Language Processing Market to reach $6bn by 2024 - PowerPoint PPT Presentation

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Cloud Natural Language Processing Market to reach $6bn by 2024

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More Information @ Recognition technology is estimated to account for major share in the global cloud NLP market owing to wide spread adoption of the image recognition, interactive voice recognition and optical character recognition technology among large and small enterprises for machine translation and information extraction. Furthermore, growing demand of recognition technology among organizations to capture and analyze customer voice for enhancing customer experience and automation is also estimated to back the growth of the recognition technology. – PowerPoint PPT presentation

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Title: Cloud Natural Language Processing Market to reach $6bn by 2024


1
Cloud Natural Language Processing Market to reach
6bn by 2024 Global Market Insights Inc.
2
Industry Overview
  • Exponential growth of the digital data is one of
    the major factors that is driving the growth of
    the cloud natural language processing market.
    Data/information has emerged as one of the most
    important assets of the organizations. Companies
    are collecting, analyzing, and reporting vast
    volume of data for extracting meaningful insights
    to get competitive edge. Growing adoption of the
    big data technologies such as Hadoop and IoT
    among organizations along with popularity of
    cloud deployment is one of the major factors that
    are fueling the growth of the digital data.
    Digital data is estimated to grow at a rate of
    40 for next 10 years and by the year 2020 data
    generation is expected to reach 44 zettabytes per
    year. As the data increases, it leads to the
    requirement of an effective analytics solution to
    process the information, therefore, driving the
    cloud NLP market growth.
  • Healthcare applications are estimated to exhibit
    higher growth rate than other industry verticals.
    Healthcare organizations are widely adopting NLP
    solutions for analyzing clinical transcriptions,
    managing medical notes, and documentation
    purposes. Furthermore, these solutions also
    assist in reducing medication errors and adverse
    drug events by managing Health Information
    Management (HIM) systems.

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Continue
  • Media advertisement and the retail sector is
    also one of the major end-users of the cloud NLP
    market solutions. They are leveraging NLP
    capabilities along with social media analytics
    modules and CRM solutions for lead generation and
    brand recognition. Furthermore, increasing
    interest of the market players towards these
    industry verticals is also backing the growth of
    the market. For instance, IBM, HP, SAS and
    EasyAsk are providing NLP tools such as sentiment
    analysis and text analysis to these industry
    sectors.
  • Asia Pacific is estimated to be the fastest
    growing region in the global cloud natural
    language processing market owing to the
    increasing adoption of the smart devices.
    Moreover, increasing investment in AI by Chinese
    players such as Baidu and Alibaba are also
    contributing significantly towards the revenue
    growth.

4

Browse Full Market Research Report Of Cloud
Natural Language Processing (NLP) Market _at_
http//bit.ly/2AgM64k Request for a Sample
of this Research report _at_ https//www.gminsights.c
om/request-sample/detail/2202
5
U.S. Cloud NLP Market Size, By Product, 2016
2024 (USD Million)

6
Industry Coverage
7
Continue
  • Some of the vendors offering NLP solutions are
    Google, Microsoft, IBM, HP, AWS, Baidu, Dolbey
    Systems, Facebook, Netbase Solutions, Fuji Xerox,
    Lexalytics, SAS, and Verint Systems. Vendors are
    trying to capture the market with customized
    product portfolio, that will help them in gaining
    more market share. Also, they are collaborating
    with cloud providers to offer products and
    services especially, to SMEs. The market is quite
    attractive as majority of the players are ready
    to invest in technologies such as AI, deep
    learning, and machine learning.

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