Natural Language Processing (NLP) Market worth $49.4 billion by 2027, growing at a CAGR of 25.7% Report by MarketsandMarkets™

Chicago, Aug. 21, 2023 (GLOBE NEWSWIRE) -- The Natural Language Processing (NLP) Market size to grow from USD 15.7 billion in 2022 to USD 49.4 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 25.7% during the forecast period, according to a new report by MarketsandMarkets™. Various factors such as rising demand for cloud-based NLP solutions to reduce overall costs and enhance scalability, demand for enterprise solutions to streamline business operations for better customer experience and urge for predictive analytics to reduce risks and identify growth opportunities are expected to drive the adoption of NLP.

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324 - Tables
68 - Figures
336 - Pages

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Scope of the Report

According to IBM, Natural language processing (NLP) refers to the branch of computer science in particular it is the branch of artificial intelligence (AI) concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, ML, and deep learning models.

By component, the NLP market has been segmented into solutions and services. The services segment is expected to grow at a rapid pace during the forecast period . For the effective working of any software, services need to be installed to increase the efficiency of the entire process. The services considered in the report are managed services and professional services. Companies such as SAS Institute, Microsoft, and IBM have started providing platforms for embedding NLP technologies with the release of speech recognition solutions and APIs.

The NLP solutions has been segmented by type into rule-based, statistical, and hybrid. The hybrid natural language processing to witness the highest CAGR during forecast period. Hybrid-based NLP is the combination of rule-based NLP and statistical-based NLP. It uses both approaches to provide better results. It can also be used to analyze a huge amount of data.

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The NLP market by organization size has been categorized into SMEs and large enterprise. The SMEs is expected to have the highest growth rate during the forecast period. The scalable functionality of NLP solutions provided by certain NLP vendors, such as Microsoft and AWS helps the SMEs to implement strategies without investing in the infrastructure and reaping on profits attained using their solutions. The SMEs are at an early stage in terms of NLP adoption, with most small enterprises experimenting in the exploratory stage, but they have started showing a greater interest in getting an analytics platform to reap desired outcomes. Most SMEs now view NLP as a strategic initiative and central IT function.

The NLP market has been segmented into five major regions: North America, Europe, Asia Pacific, Latin America, and Middle East & Africa. Asia Pacific is expected to hold the highest CAGR during the forecast period. Some large multinational players are showing interests to open new offices in the region, and a lot of new entrepreneur setups are taking place, who are adopting the advanced AI technologies to have a competitive advantage over the established players. China, Japan, and India have shown ample growth opportunities by adopting NLP solutions in the market.

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Top Trends in Global Natural Language Processing (NLP) Market

• Transformer-based models, illustrated by OpenAI's GPT series (such as GPT-3), dominated the NLP scene. These models performed admirably across a wide range of NLP tasks and were being used in a variety of applications, including chatbots, content production, translation, sentiment analysis, and others.
• There has been a growing emphasis on developing and deploying NLP models capable of understanding and producing material in various languages. To ease communication and content development across diverse linguistic communities, multilingual models were being deployed.
• Pretrained models such as BERT (Bidirectional Encoder Representations from Transformers) have become popular. These models were pretrained on vast datasets before being fine-tuned for individual applications, eliminating the requirement for further training and greatly enhancing performance.
• NLP-powered chatbots and conversational AI were gaining acceptance across industries. These technologies were being used by businesses to improve customer service, automate regular interactions, and deliver personalised user experiences.
• As the use of NLP models increased, there was more concern about potential biases and ethical implications in AI-generated material. Bias, fairness, and transparency issues in NLP applications were being addressed by researchers, developers, and organisations.
• Researchers were investigating methods to enable NLP models to execute tasks with little or no task-specific training data, referred to as zero-shot and few-shot learning. This was supposed to result in more versatile and flexible NLP systems.
• The healthcare industry was aggressively investigating NLP applications for activities such as medical record analysis, disease diagnosis, drug discovery, and patient contact. NLP was viewed as a method for extracting useful information from unstructured medical data.
• Financial institutions used NLP for sentiment analysis of market data, fraud detection, risk assessment, and customer interaction analysis. NLP models aided in the processing of enormous amounts of financial text data for decision-making.
• NLP models were used by content creators and marketers to help them generate high-quality material such as articles, social media postings, and product descriptions. The efficiency and inventiveness of AI-generated material fueled this trend.
• NLP was expanding beyond text processing and into speech processing. Speech recognition and speech production technology were improving, paving the way for transcription services, voice assistants, and other uses.

Key Industry Development in Natural Language Processing (NLP) Market

• There may have been more advances in pretrained NLP models, with larger and more powerful models being introduced. Researchers and businesses may have continued to push the limits of model size and performance, resulting in ever more amazing results on numerous NLP tasks.
• More emphasis could have been placed on the integration of NLP with other modalities such as vision and audio. Text-based hybrid models that integrate text with various types of data may have been created for applications such as image captioning, video analysis, and others.
• NLP models capable of understanding and producing information in a variety of languages may have become increasingly common. Efforts to improve multilingualism and minimise language barriers may have resulted in innovative applications and expanded global reach.
• The industry may have placed a higher priority on tackling ethical concerns and prejudice in NLP models. Researchers and organisations may have tried to provide tools and approaches for detecting and mitigating biases in AI-generated content.
• NLP might have been used more extensively in the healthcare sector for tasks like as clinical document analysis, medical image processing, patient interaction, and medication development. NLP could have played a larger role in assisting medical professionals and researchers.
• Conversational AI systems, such as chatbots and virtual assistants, could have become more sophisticated and capable of handling complex interactions. Natural language comprehension and generation may have increased, resulting in more natural and productive dialogues.
• With the advent of intelligent tutoring systems, automated essay grading, language learning platforms, and other applications, NLP applications in education could have expanded.
• NLP technology may have been used for social good purposes such as disaster response, humanitarian relief, and solving global issues such as climate change and public health crises.
• Governments and regulatory authorities may have begun to develop guidelines and restrictions for the use of NLP in a variety of businesses, particularly in areas with substantial societal effect and ethical concerns.
• Industry Consolidation and Partnerships: As organisations attempted to strengthen their capabilities and expand their services in the quickly expanding NLP landscape, the NLP market may have seen mergers, acquisitions, and partnerships.

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