Friday, December 2, 2022

The Natural Language Processing (NLP) Market in 2022

Natural language processing (NLP) is the intersection of computer science, artificial intelligence, and linguistics. It focuses on the way 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, speech-to-text recognition, and real-time language translations.

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

Natural language processing market

The natural language processing market had an estimated value of $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’s set to reach $25.7 billion by the end of it. 

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

  • The U.S. market had an estimated value of $3.8 billion in 2020
  • The Chinese market is forecast to maintain a CAGR of 9.6%, reaching $4.5 billion in 2027
  • Japan and Canada are each expected to see a CAGR of 9.4% and 8.5% over the forecast period from 2020 to 2027
  • In Europe, Germany is forecast for 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 industry, the natural language processing market is led by the high tech and telecom industries at 22.8%, followed by the banking, financial services, and insurance (BFSI) industry.

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 tasked with analyzing and understanding the patterns, structures, and use cases of human language, whether spoken or written.

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

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

Keyword recognition-based systems

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

The system searches for specific keywords that are connected to predetermined actions and services without necessarily understanding the entirety of the request.

Rules-based systems

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

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

ML-based systems

By depending on machine learning and deep learning algorithms, smart NLP systems can be made smarter the more time and data they’re given to train. Depending on the type of algorithm used to train them, smart systems are able to detect patterns in human speech and make accurate predictions, especially in a specific field.

Unlike the previous models, ML-based models don’t rely on keywords or rules. They read and process the entirety of a sentence or paragraph and attempt to extract useful meaning based on its learned experience.

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

  • Speech recognition
  • Natural language generation
  • Emotion analysis
  • Text summarization
  • Speech tagging
  • Entity recognition
  • Next-word prediction

After technological advancements over the years, “NLP could easily outdo average humans in many tasks and in some cases, even exceed the performance of subject matter experts,” says Narendran Thillaisthanam, VP of emerging technologies at Vuram, to IT Business Edge.

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

Benefits of natural language processing

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

When applied in a business setting, NLP can have numerous benefits, such as:

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

“Advances in NLP have enabled us to extract the semantics (meaning) of discourse contextually in natural language; you can use it to read through agent-customer sessions to find out what the issue was, if it got resolved, and, if not, how dissatisfied the customer is,” says Deepak Dube, a member of the Forbes Technology Council.

“Couple this with machine learning for as many customer touchpoints in your enterprise as possible, and you can gain deep visibility into your customers.”

Natural language processing use cases

See how several organizations in different industries are using NLP:

Nebraska Medicine

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

With the thousands of codes used in medical records, the hospital faced issues with over-coding and under-coding, resulting in inconsistent records with higher denial rates. The misuse of billing and diagnosis codes also resulted in lost revenue for the hospital.

Looking to automate its coding operations, Nebraska Medicine used the CodeRyte CodeAssist System, 3M’s natural language processing web-paced solution.

“The billing goes through faster, it’s clean, and it’s correct the first time,” says Terri Nelsen, professional coding manager, Nebraska Medicine.

“We’ve typically seen about a 20% reduction in the amount of time that the coders are spending on notes, which means we can get about 20% more coder productivity than before.”

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

Equals 3

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

Developing its new market analysis solutions, Equals 3 was looking to implement high cognitive capabilities into its Lucy software for handling 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 far more robust and varying in what it offers, and IBM has a specific roadmap for continuing to develop cognitive offerings,” says Marc Dispensa, CTO, Equals 3.

“Competitors don’t have the cognitive functionality that IBM has — IBM stands alone, and the support that 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 the road to market.

schuh

schuh is a chain of stores specializing in a range of casual and sports footwear, in addition to schuh’s own line of products. Headquartered in Scotland, schuh runs over 120 stores in the U.K. and Ireland.

Customer experience is of utmost importance to schuh. However, the company had trouble properly receiving and managing customer complaints and feedback that would help it better serve its customers.

schuh’s support center started using Amazon Comprehend’s NLP and ML capabilities to analyze incoming customer emails. This simplified the process of color-coding issues before passing them on to customer care.

“Using Comprehend to put a customer problem in front of the right person really gives us the best chance of retaining that customer going forward,” says Blair Milligan, head of systems development, schuh.

“These things can mean more to people than salary. If you can give someone a job where they’re excited, that’s a huge currency. We talk about Comprehend and Forecast and other AWS services when we are interviewing.”

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

Natural language processing providers

Some of the leading providers 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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