Natural language processing (NLP) is the intersection of computer science, artificial intelligence, and linguistics. It focuses on how computers can process and analyze data in the form of human languages.
Natural language processing solutions are used where 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 worth $13 billion in 2020. It is expected to maintain a compound annual growth rate (CAGR) of 10.3% during the forecast period of 2020 to 2027, and is expected to reach $25.7 billion by the end of it.
On a regional basis, 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 forecast to maintain a CAGR of 9.6%, reaching $4.5 billion in 2027
- Japan and Canada are expected to register a CAGR of 9.4% and 8.5% during the forecast period of 2020 to 2027
- In Europe, Germany is forecast to have 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 led by the high-tech and telecommunications industries with 22.8%, followed by the banking, financial services and insurance (BFSI) industry.
Other notable industries include:
- Health care
- life sciences
- Retail and eCommerce
- Advertising and media
Natural language processing functions
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 produce more accurate translations of human languages, understanding human languages also allows software to translate them into actionable commands and various computer languages.
As part of the field of artificial intelligence, natural language processing tools are often developed in various ways:
Keyword recognition based systems
Keyword recognition and extraction in NLP follow specific rules set by the developers.
The system searches for specific keywords that are connected to predetermined actions and services without necessarily understanding the entirety of the request.
rule based systems
Instead of scanning input for a specific keyword from a predetermined list, rule-based systems attempt to understand the entirety of input by reviewing a library of pre-programmed human language rules and examples.
While still limited in capabilities and accuracy, rule-based NLP systems can be made smarter and more efficient with larger libraries of labeled data.
By relying on machine learning and deep learning algorithms, smart NLP systems can get smarter the more time and data they are given to train. Depending on the type of algorithm used to train them, intelligent systems can detect patterns in human speech and make accurate predictions, especially in a specific field.
Unlike previous models, ML-based models are not based on keywords or rules. They read and process the whole of a 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, rather than trying to understand a language across multiple concepts and input methods. Some tasks include:
- Speech recognition
- Natural language generation
- emotion analysis
- text summary
- voice tagging
- entity recognition
- next word prediction
After technological advances over the years, “NLP could easily outperform average humans on many tasks, and in some cases even outperform experts in the field.” says 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 business (transformational) benefits, while promising to become mainstream in less than two years.”
Benefits of Natural Language Processing
The main goal of natural language processing applications is to facilitate communication between humans and computers through text or voice.
When applied in a business setting, NLP can have numerous benefits, such as:
- Large-scale text data analysis
- Increase productivity
- Automation of processes in real time
- Improve customer experience
- cost reduction
- Partial automation of research
- content moderation
“Advances in NLP have allowed us to extract the semantics (meaning) of speech contextually in natural language; you can use it to read agent-customer sessions to find out 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 this with machine learning for as many customer touchpoints in your company as possible, and you can get deep visibility into your customers.”
Natural language processing use cases
See how various organizations in different industries are using NLP:
Nebraska Medicine is an academic health system, with more than 1,000 physicians and 40 primary and specialty care clinics with more than 800 licensed beds.
With the thousands of codes used in medical records, the hospital faced issues of overcoding and undercoding, resulting in inconsistent records with higher denial rates. Misuse of billing and diagnosis codes also resulted in a loss of revenue for the hospital.
Looking to automate its coding operations, Nebraska Medicine used CodeRyte CodeAssist System, 3M’s natural language processing web solution.
“Invoicing is processed faster, is clean and correct the first time”, says Terri Nelsonprofessional coding manager, Nebraska Medicine.
“Overall, we’ve seen a 20% reduction in the amount of time developers spend on notes, which means we can get 20% more productivity than before.”
By adopting the 3M NLP solution, Nebraska Medicine was able to increase revenue by 25%, boost coder productivity by 30%, and reduce revenue cycle time by 20 days.
is 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 analysis solutions, Equals 3 was looking to implement high cognitive capabilities into its Lucy software to handle massive amounts of structured and unstructured data.
Launching its Lucy platform in the IBM 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 for continuing to develop cognitive offerings.” says Marc DispensaCTO, equal to 3.
“Competitors don’t have the cognitive functionality that IBM has; IBM is independent and the support we receive from IBM is the best, bar none.”
With IBM, Equals 3 was able to drive global expansion to improve its services and accelerate its path to market.
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 operates more than 120 stores in the UK and Ireland.
Customer experience is of paramount importance to schuh. However, the company struggled to properly receive and manage customer complaints and feedback that would help it better serve its customers.
Schuh’s support center began using Amazon Comprehend’s NLP and ML capabilities to analyze incoming customer emails. This simplified the color coding process before passing them on to customer service.
“Using Comprehend to put a customer issue in front of the right person really gives us the best chance of retaining that customer in the future.” Blair Milligan sayshead of systems development, schuh.
“These things can mean more to people than salary. If you can give someone a job they’re excited about, that’s a big coin. 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 deliver faster, more refined customer service.
Natural Language Processing Providers
Some of the leading providers in the natural language processing market include:
- google cloud
- Dolby Systems
- Verint Systems
- invent technologies