Natural language processing (NLP) refers to the branch of computer science, and more specifically, the branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in the same way human beings can.
NLP combines computational linguistics rule-based modelling of human language with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to understand its full meaning, complete with the speaker or writer’s intent and sentiment.
NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly, even in real time. Interactions with NLP are in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chat-bots, and other consumer conveniences. But NLP also plays an important role in enterprise solutions to help streamline business operations, increase employee productivity, and simplify mission-critical business processes.
COVID-19 Scenario Analysis:
The cloud natural language processing market witnessed a slowdown in 2020 due to the COVID-19 pandemic. The COVID-19 pandemic increased the churn rate and shuddered almost every business vertical. The lockdown impacts global manufacturing, and supply chain and logistics as the continuity of operations for various verticals get badly impacted. The verticals facing the greatest drawbacks include manufacturing, transportation and logistics, and retail and consumer goods. The availability of essential items is impacted due to the lack of manpower to work on production lines, supply chains, and transportation, although the essential items are exempted from the lockdown. The condition is expected to come under control by early 2021, while the demand for NLP solutions and services is expected to increase due to the rise in demand for enhancing customer experiences and building personalized relationships with prospects. Several verticals are already planning to deploy a diverse array of NLP solutions and services for enabling digital transformation initiatives, which address mission-critical processes, improve operations, and differentiate customer viewing experiences. The reduction in operational costs, better customer experiences, improved customer churn rate, enhanced visibility into processes and operations, and improved real-time decision-making are key business and operational priorities expected to drive the adoption of NLP.
Top Impacting Factors: Market Scenario Analysis, Trends, Drivers, and Impact Analysis
With the advancement in the internet of things (IoT) and communication technologies, it has become possible to set up communication between various devices. Such concept has grown to facilitate a smart home environment and connected vehicles. Technological advancement and digital transformation have changed the way industries perform their operations and communicate with their customers. NLP proves extremely beneficial in facilitating interactions between users and systems or machines. The smart device includes a mobile phone, an industrial device installed in a plant, or a device operating home/building environment. The rise in demand for voice-based solutions interfaces with NLP-based applications offers users enhanced functionalities, such as the verbal command capability with instant query management. The smart home consists of numerous smart devices, such as thermostats, lights, security and monitoring devices, and climate control devices. Consumers prefer voice mode to give commands to such smart devices.
Code-switching or Code-Mixed (CM) language is the alternation of languages within a conversation or utterance and is a common communicative phenomenon that occurs in multilingual communities across the world. Traditionally, CM language is associated with informal or casual speech. There is evidence that in several societies, such as urban India and Mexico, CM language has become the default code of communication. It has also pervaded written text, especially in computer-mediated communication and social media. NLP tasks, such as normalization, language identification, language modelling, part-of-speech tagging, and dependency parsing, machine translation, and Automatic Speech Recognition (ASR), face issues while working on non-canonical multilingual data, in which two or more languages are mixed. The characteristics of mixed data affect tasks in different ways, sometimes by changing the definition (for example, in language identification, the shift from document-level to word-level), and sometimes by creating new lexical and syntactic structures (for example, mixed words that consist of morphemes from two different languages).
Key Benefits of Report:
- This study presents the analytical depiction of the industry along with the current trends and future estimations to determine the imminent investment pockets.
- The report presents information related to key drivers, restraints, and opportunities along with detailed analysis of the cloud natural language processing market share.
- The current market is quantitatively analyzed to highlight the cloud natural language processing market growth scenario.
- The report provides a detailed market analysis based on competitive intensity and how the competition will take shape in the coming years.
Questions Answered in Cloud Natural Language Processing (NLP) Market Report:
- Which are the leading market players active in the cloud natural language processing market?
- What would be the detailed impact of COVID-19 on the market?
- What current trends would influence the cloud natural language processing market in the next few years?
- What are the driving factors, restraints, and opportunities in the cloud natural language processing market?
- What are the projections for the future that would help in taking further strategic steps?
Cloud Natural Language Processing (NLP) Market Report Highlights
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 Key Market Players | Inbenta Holdings Inc., Baidu, Inc., IBM Corp., Microsoft Corp., Meta, SAS Institute Inc., Apple Inc.,, 3M, Intel Corporation, Amazon Web Services, Inc., Google LLC |
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