The natural language processing (NLP) market in 2022

Natural language processing (NLP) is at the intersection of computer science, artificial intelligence and linguistics. It focuses on how computers are able to process and analyze data in the form of human languages.

Natural language processing solutions are used wherever languages ​​are needed to interact with complex computer algorithms. They can be found in voice command software, voice recognition, and real-time language translations.

See below to know all about the global natural language processing market:

natural language processing market

The natural language processing market had an estimate worth $13 billion in 2020. Expected to maintain a compound annual growth rate (CAGR) of 10.3% over the forecast period from 2020 to 2027, it is expected to reach $25.7 billion by the end.

Regionally, the natural language processing market is segmented as follows:

  • The US market was worth an estimated $3.8 billion in 2020
  • The Chinese market is expected to maintain a CAGR of 9.6%, reaching $4.5 billion in 2027
  • Japan and Canada are each expected to experience a CAGR of 9.4% and 8.5% during the forecast period of 2020 to 2027
  • In Europe, Germany forecasts a CAGR of 8.4% from 2020 to 2027
  • The Asia-Pacific market, led by Australia, India and South Korea, is expected to reach $3 billion by 2027

By industrythe natural language processing market is dominated by the high-tech and telecommunications sectors at 22.8%, followed by the banking, financial services and insurance (BFSI) sector.

Other notable industries include:

  • Health care
  • Life sciences
  • Retail and e-commerce
  • Automotive
  • Transportation
  • Advertising and media
  • Manufacturing

Natural language processing features

Natural language processing tools are responsible for analyzing and understanding the patterns, structures, and use cases of human language, whether spoken or written.

While language processing can help produce more accurate translations of human languages, understanding human languages ​​also allows software to translate them into actionable commands and various computer languages.

In the field of artificial intelligence, natural language processing tools are often developed in several ways:

Systems based on keyword recognition

Keyword recognition and extraction in NLP follows specific rules set by developers.

The system searches for specific keywords that are related to predetermined actions and services without necessarily understanding the entire request.

Rule-Based Systems

Instead of searching for a specific keyword from a predetermined list, rule-based systems attempt to understand the entire input by going through a library of human language rules and pre-programmed examples.

Although still limited in their capabilities and accuracy, rule-based NLP systems can be made smarter and more efficient with larger libraries of labeled data.

ML-based systems

By leveraging machine learning and deep learning algorithms, smart NLP systems can be made smarter as they have more time and data to train with. Depending on the type of algorithm used to train them, intelligent systems are able to detect patterns in human speech and make accurate predictions, especially in a specific domain.

Unlike previous models, ML-based models do not rely on keywords or rules. They read and process an entire sentence or paragraph and attempt to extract useful meaning based on their learned experience.

Natural language processing systems tend to be hyper-specialized in a specific task, instead of trying to understand a language across multiple concepts and input methods. Some tasks include:

  • Speech recognition
  • Natural language generation
  • Emotion analysis
  • Summary of text
  • Voice tagging
  • Entity recognition
  • Next word prediction

After technological advancements over the years, “NLP could easily outperform average humans in many tasks and, in some cases, even surpass the performance of subject matter experts”, said Narendran ThillaisthanamVice President of Emerging Technologies at Vuram, at IT Business Edge.

“According to Gartner, technologies such as conversational AI, chatbots, and document AI are expected to deliver high to very high (transformational) business benefits, while promising to go mainstream in less than two years.”

Benefits of natural language processing

The main purpose of natural language processing applications is to facilitate communication between humans and computers through text or speech.

When applied in a professional context, NLP can have many benefits, such as:

  • Large-scale textual data analysis
  • Boost productivity
  • Real-time process automation
  • Improve the customer experience
  • Cost reduction
  • Partial search automation
  • Content moderation

“Advances in NLP have allowed us to extract semantics (meaning) from speech contextually in natural language; you can use it to read agent-customer sessions to determine what the problem was, if it was resolved, and if not, how dissatisfied the customer is,” says Deepak Dubemember of the Forbes Technology Council.

“Combine that with machine learning for as many customer touchpoints in your business as possible, and you can gain deep visibility into your customers.”

Natural Language Processing Use Cases

Discover how several organizations from different sectors are using NLP:

Nebraska Medicine

Nebraska Medicine is an academic health system, with more than 1,000 physicians and 40 specialty and primary care clinics with more than 800 licensed beds.

With the thousands of codes used in medical records, the hospital faced issues with overcoding and undercoding, resulting in inconsistent records with higher rejection rates. Misuse of billing and diagnostic codes also resulted in lost revenue for the hospital.

Seeking to automate its coding operations, Nebraska Medicine used CodeRyte CodeAssist, 3M’s web-based natural language processing solution.

“Billing goes faster, it’s clean and it’s right the first time,” said Terri NelsenProfessional Coding Manager, Nebraska Medicine.

“We’ve typically seen about a 20% reduction in the time coders spend on notes, which means we can achieve about 20% more coder productivity than before.”

By adopting 3M’s NLP solution, Nebraska Medicine was able to increase revenue by 25%, increase coder productivity by 30%, and reduce revenue cycle time by 20 days.

Equal to 3

Equals 3 is a software company that helps clients with marketing data and turns insights into actionable strategies.

In developing its new market analytics solutions, Equals 3 sought to implement high cognitive capabilities in its Lucy software to handle massive amounts of structured and unstructured data.

Launching its Lucy platform in IBM’s cloud environment, Equals 3 also used IBM Watson to make the interface accessible with natural language queries.

“IBM technology is much more robust and varies in what it offers, and IBM has a specific roadmap to continue developing cognitive offerings,” says Marc DispensaCTO, equal to 3.

“Competitors don’t have the cognitive functionality that IBM has; IBM is self-contained, and the support we get from IBM is the best, bar none. »

With IBM, Equals 3 was able to drive global expansion to improve its services and accelerate time to market.


Schuh is a chain store specializing in a range of casual and athletic shoes, in addition to Schuh’s own product line. Based in Scotland, Schuh operates over 120 stores in the UK and Ireland.

Customer experience is of utmost importance to schuh. However, the company struggled to properly receive and handle customer complaints and feedback that would help it better serve its customers.

Schuh’s help desk has started using Amazon Comprehend’s NLP and ML capabilities to analyze incoming customer emails. This simplified the process of color coding issues before escalating to customer service.

“Using Comprehend to bring a customer issue to the right person really gives us the best chance of retaining that customer in the future,” says Blair Milligansystems development manager, schuh.

“These things can mean more to people than salary. If you can give someone a job they’re interested in, that’s a huge bargaining chip. We talk about Comprehend and Forecast and other AWS services in our interviews.

Using AWS Comprehend, schuh was able to increase staff productivity and provide faster, more polished customer service.

Natural language processing providers

Some of the key vendors in the Natural Language Processing Market include:

  • 3M
  • Google Cloud
  • IBM
  • Dolbey Systems
  • SAS
  • Verint Systems
  • SparkCognition
  • AWS
  • Microsoft
  • Inbenta Technologies

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