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1624508

2024 年至 2031 年自然語言處理 (NLP) 市場類型、部署模式、應用程式、最終用戶和地區

Natural Language Processing Market By Type, By Deployment Mode, By Application, By End Users (Healthcare, Banking, Financial Services, And Insurance, Media And Entertainment, E-commerce), And Region For 2024-2031

出版日期: 2024年09月23日 | 出版商:

Verified Market Research | 英文 202 Pages | 商品交期: 2-3個工作天內

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簡介目錄

自然語言處理 (NLP) 市場估值，2024 年至 2031 年

自然語言處理(NLP) 市場規模預計在2023 年將達到317.6 億美元，預計到2031 年將達到929.9 億美元，2024 年至2031 年的複合年增長率為23.97%。達到美國10萬美元。醫療保健、銀行、零售和客戶服務等各行業對 NLP 技術的廣泛應用為市場成長做出了重大貢獻。 NLP 評估大量非結構化資料並提取競爭見解的能力對於尋求改善決策流程和獲得競爭優勢的企業來說至關重要。語音啟動虛擬助理和聊天機器人的興起正在推動需求並加速消費市場中 NLP 應用的成長。

自然語言處理 (NLP) 市場定義/概述

自然語言處理（NLP）是一種使用人工智慧來解釋人類語言的電腦應用程式。這項電腦技術允許電腦使用一系列技術和理論來研究和解釋人類的交流。自然語言處理（NLP）的目標是減少理解Ruby、C、C++和Java等電腦語言所需的時間。在當今的商業世界中，NLP 用於大數據分析，因為大量數據來自語音、電子郵件、網路部落格、文件、社交網站、論壇等來源。

光學字元辨識 (OCR)、自動編碼、文字分析、互動式語音應答 (IVR)、模式和影像辨識、分類和歸類以及語音分析都是自然語言處理 (NLP) 技術的例子。自然語言處理 (NLP) 可以基於雲端或本地部署，並被汽車、零售、消費品、高科技、電子、政府、銀行、金融服務和保險 (BFSI)、醫療保健等各行各業廣泛使用。、研究、教育、媒體、娛樂等各行業，用於資訊擷取、問答、機器翻譯和報告生成等應用。

哪些因素推動了自然語言處理（NLP）市場的成長？

預計自然語言處理 (NLP) 市場將因高級文本分析需求的不斷增長而得到推動。透過將自然語言處理 (NLP) 與文本分析相結合，正在開發高級文本分析解決方案。自然語言處理 (NLP) 有助於將人類語言轉換為機器碼。這有助於數據處理並將想法翻譯回人類語言以便於理解。為了獲得更好的市場洞察力，各個組織對文字分析的需求不斷增長，這推動了對文字分析解決方案的需求。為了提高獲利能力，各行各業的公司都努力掌握市場趨勢，並提供順應新趨勢的產品和服務。

社群媒體管道在自然語言處理 (NLP) 市場的成長中發揮著至關重要的作用。許多行業競爭日益激烈，要求即時洞察市場，瞭解客戶需求和其他因素。企業必須評估大量非結構化資料才能獲得即時的市場洞察。自然語言處理 (NLP) 系統需要將非結構化的人類語言輸入轉換為機器代碼，同時深入瞭解人類語言的情緒和情緒。

人工智慧和機器學習技術的進步進一步推動了自然語言處理 (NLP) 市場的成長。這些進步不斷提高自然語言處理 (NLP) 系統的準確性和性能，增強其理解和解釋人類語言的能力。各行業數位助理和聊天機器人的興起正在推動 NLP 解決方案的採用，以實現更無縫的人機互動。隨著這些技術越來越多地融入日常工作中，對自然語言處理 (NLP) 解決方案的需求預計只會增加。

自然語言處理（NLP）市場面臨哪些課題？

一個大問題是，人類語言複雜且充滿了微妙之處、文化差異和機器難以理解的細微差別。機器很難準確地掌握語言的含義，因為相同的單字和短語往往具有不同的含義，發音相同的單字也可能具有不同的含義。另一個問題是自然語言處理 (NLP) 系統需要許多不同類型的資料才能有效地訓練。此外還存在隱私和道德課題。使用個人資料來訓練 NLP 系統會引發道德問題和隱私問題。

為了維持相關性和實用性，NLP 系統需要定期更新和變更。確保 NLP 系統不帶偏見並產生公平的結果非常重要。否則，可能會加劇現有的社會問題。應對這些課題需要不同學科的人們共同努力，繼續研究和學習如何以合乎道德和負責任的方式建構人工智慧。

The Natural Language Processing Market size was valued at USD 31.76 Billion in 2023 and is projected to reach USD 92.99 Billion by 2031 , with a growth rate (CAGR) of 23.97 % from 2024 to 2031 . The growing use of NLP technology in a variety of industries, including healthcare, banking, retail, and customer service, has contributed significantly to market growth. NLP's ability to evaluate and extract insights from massive volumes of unstructured data has become critical for businesses looking to improve decision-making processes and gain a competitive advantage. The rise of voice-activated virtual assistants and chatbots has increased demand for NLP applications in the consumer market, accelerating growth.

Natural Language Processing Market: Definition/Overview

Natural language processing (NLP) is a computer application that uses artificial intelligence to interpret human language. This computerized technique allows the computer to examine and interpret human communication using a collection of technologies and theories. The purpose of natural language processing is to reduce the time required to grasp computer languages like Ruby, C, C++, and Java. NLP is used in big data analysis because huge amounts of data are generated in today's business scenarios from sources such as audio, emails, web blogs, documents, social networking sites, and forums.

Optical character recognition (OCR), auto coding, text analytics, interactive voice response (IVR), pattern and image recognition, classification and categorization, and speech analytics are all examples of natural language processing technology. Natural language processing (NLP) can be cloud-based or on-premise, and it is used for applications such as information extraction, question answering, machine translation, and report generation in a variety of industries, including automotive, retail, and consumer goods, high-tech and electronics, government, banking, financial services, and insurance (BFSI), health care and life sciences, research and education, and media and entertainment.

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What Factors are Fueling the Growth of Natural Language Processing Market?

The market for natural language processing is expected to be boosted by the rising demand for advanced text analytics. Integration of natural language processing into text analytics has resulted in the development of advanced text analytics solutions. Natural language processing facilitates the conversion of human language to machine language. This aids in data processing and translates ideas back into human language for easier comprehension. The increasing demand for text analytics in various organizations to acquire better market insights has pushed the demand for text analytic solutions. Companies across industries are continually tracking the market pulse and tailoring their product and service offerings to capitalize on emerging trends for increased profitability.

Social media channels play an important part in the growth of the Natural Language Processing Market. Increased competition in numerous verticals has created a desire for real-time market insights into client demands and other factors. Companies must evaluate vast volumes of unstructured data to acquire real-time market insights. Natural language processing systems are required to translate unstructured human language input into machine language while also gaining insights into human language feelings and emotions.

The growth of the Natural Language Processing Market is further fueled by advancements in artificial intelligence and machine learning technologies. These advancements enable NLP systems to continually improve their accuracy and performance, leading to enhanced capabilities in understanding and interpreting human language. The proliferation of digital assistants and chatbots across various industries is driving the adoption of NLP solutions for more seamless human-computer interactions. As these technologies become increasingly integrated into everyday business operations, the demand for natural language processing solutions is expected to continue its upward trajectory.

What are the Challenges Faced by Natural Language Processing Market?

One big issue is that human languages are complex, with many subtle differences, cultural variations, and nuances that are hard for machines to understand. It's tricky for machines to accurately grasp the meaning of language because there are often different meanings for the same word or phrase, and words that sound alike might mean different things. Another problem is that NLP systems need lots of different kinds of data to train them effectively, and the quality and quantity of that data are really important for how well the system works. There's also the challenge of privacy and ethics - using personal data to train NLP systems raises ethical concerns and privacy issues.

For NLP systems to stay relevant and useful, they need to be updated and changed regularly because technology is always advancing. It's important to make sure that NLP systems don't have biases and that they're fair in the results they give because if they're not, they can make existing social problems worse. To deal with these challenges, people from different fields need to work together, and we need to keep studying and learning about how to make AI that's ethical and responsible.

Category-Wise Acumens

How is the Hybrid Cloud Category Predicted to Develop the Quickest in the Natural Language Processing Market Throughout the Forecast Period?

The hybrid cloud category is expected to experience the fastest growth in the natural language processing (NLP) market over the projection period due to its unique ability to effortlessly incorporate the benefits of both public and private cloud settings. As enterprises realize the value of harnessing NLP capabilities for better communication and data analysis, the hybrid cloud model emerges as a strategic solution. This method enables enterprises to benefit from the scalability and cost-effectiveness of public clouds while retaining the protection and control provided by private clouds. The hybrid cloud's versatility meets the varying needs of organizations, allowing for the effective deployment of NLP applications across several domains. This versatility and flexibility are projected to drive significant adoption, putting the hybrid cloud category at the forefront of NLP industry growth for the foreseeable future.

The rapid development of the hybrid cloud category in the natural language processing (NLP) market is also attributed to its capability to address the evolving demands of enterprises. With the increasing complexity of NLP applications and the need for seamless integration with existing IT infrastructure, the hybrid cloud offers a solution that balances performance, security, and scalability. The hybrid cloud model facilitates compliance with regulatory requirements and data governance standards, further enhancing its appeal to organizations operating in regulated industries. This comprehensive approach ensures that enterprises can leverage NLP technologies effectively while mitigating potential risks associated with data privacy and security. Consequently, the hybrid cloud category is anticipated to maintain its momentum and emerge as a pivotal driver of growth in the NLP market, catering to the diverse needs of businesses across various sectors.

How did Machine Translation Dominate the Natural Language Processing Market?

The machine translation category dominates the natural language processing (NLP) market due to its critical function in breaking down language barriers and facilitating seamless communication across varied linguistic landscapes. As organizations and individuals become more involved in worldwide contacts, the demand for rapid and precise language translation solutions has grown. Machine translation systems, utilizing advances in deep learning and neural networks, have considerably improved their ability to produce high-quality translations, thereby closing the language divide. Furthermore, the ongoing advancement of machine translation algorithms, together with the incorporation of sophisticated approaches such as neural machine translation (NMT), has accelerated the category's growth.

Significant investments in research and development have been made by both public and private sectors to enhance machine translation capabilities. Breakthroughs in areas such as natural language processing, computational linguistics, and artificial intelligence have contributed to the refinement of machine translation algorithms. The extensive datasets and corpora have been compiled and utilized to train these systems, enabling them to better understand context, idiomatic expressions, and nuances in language usage.

Enterprises across a variety of industries, including e-commerce, healthcare, and finance, understand the revolutionary power of trustworthy machine translation in broadening their worldwide reach, developing international collaborations, and assuring successful cross-cultural communication. As a result, the machine translation segment has the greatest proportion of the NLP market, demonstrating its importance in today's interconnected and linguistically diverse globe.

Country/Region Wise Acumens

What are the Driving Factors Contributing to North America's Dominance in the Natural Language Processing Market?

North America's dominance in the Natural Language Processing Market is influenced by several driving factors. The region boasts a robust technological infrastructure and a high level of innovation, with leading companies and research institutions continuously pushing the boundaries of NLP technology. This environment fosters the development of cutting-edge NLP solutions and attracts significant investments from both the public and private sectors.

The region is home to a large concentration of tech-savvy enterprises across various industries, including technology, finance, healthcare, and e-commerce. These organizations recognize the transformative potential of NLP in improving customer experiences, streamlining operations, and gaining competitive advantages in the global market. As a result, there is a strong demand for NLP products and services, further driving the growth of the market in the region.

North America benefits from a well-established ecosystem of NLP talent, including researchers, engineers, and data scientists, who contribute to the continuous advancement of NLP technology. Academic institutions and research centers in the region play a pivotal role in nurturing this talent pool and conducting groundbreaking research in areas such as natural language understanding, sentiment analysis, and machine translation. The favorable government policies and regulatory frameworks in North America support the development and adoption of NLP technologies. Initiatives aimed at promoting innovation, fostering collaboration between industry and academia, and incentivizing investment in emerging technologies contribute to the overall growth and competitiveness of the NLP market in the region.

Will Asia Pacific Cause an Increase in Trade of Natural Language Processing Market?

An increase in the trade of the Natural Language Processing (NLP) market is anticipated to be driven by the Asia Pacific region. The rapid expansion of digital infrastructure and internet penetration across Asia Pacific countries has facilitated the collection of vast amounts of linguistic data, which is essential for training NLP algorithms.

Advancements in artificial intelligence and machine learning technologies have bolstered the development of more sophisticated NLP solutions, making them increasingly attractive to businesses in the region.

The growing emphasis on multilingual communication and the need to break down language barriers in diverse markets have spurred demand for NLP solutions that can accurately translate and analyze text in multiple languages. The rising adoption of NLP across various industries such as e-commerce, finance, healthcare, and customer service are fueling the expansion of the market in Asia Pacific. This is driven by the recognition of NLP's ability to enhance operational efficiency, improve customer experiences, and gain insights from unstructured data.

Overall, the Asia Pacific region's pivotal role in driving the trade of NLP solutions is underscored by its technological advancements, increasing digitalization, and growing demand for cross-lingual communication capabilities across diverse industries.

Competitive Landscape

The Natural Language Processing Market is highly competitive and consists of several major players who have been trying to gain larger shares. These major players with prominent shares in the market have been focusing on expanding their customer base across foreign countries. They are providing new innovative solutions, along with deals and mergers, to increase their market shares and profitability.

Some of the prominent players operating in the Natural Language Processing Market include:

3M, Apple Inc., Amazon Web Services Inc., Baidu Inc., Crayon Data, Google LLC, Health Fidelity, IBM Corporation, Inbenta, IQVIA, Meta Platforms Inc., Microsoft Corporation, Oracle Inc., SAS Institute Inc.

Latest Developments

In July 2023, Google AI released PaLM 2, a new LLM with 540 billion parameters. The primary goal is to improve factual language understanding and reasoning abilities.

In October 2023, OpenAI announced its decision of limiting access to its powerful LLM, GPT-4, for only selective research partners and developers.

In February 2024, Apple unveiled its advancements in on-device NLP with the announcement of latest features in its upcoming iOS version that leverage on-device processing for improved privacy and responsiveness in NLP task.

In Oct 2022, IBM expanded its embeddable AI software portfolio with the launch of several new libraries aimed to help IBM Ecosystem partners, customers, and developers build and sell their AI-powered products more easily, rapidly, and cost-effectively. The AI libraries were created by IBM Research and are intended to provide Independent Software Vendors (ISVs) from various industries with an easily scalable way to incorporate natural language processing, text-to-speech, and speech-to-text capabilities into applications running in any environment.

In Jun 2022, Apple announced plans to provide an open-source reference PyTorch version of the Transformer architecture accessible, enabling developers globally to install Transformer models on Apple devices easily.

1 INTRODUCTION OF GLOBAL NATURAL LANGUAGE PROCESSING MARKET

1.1 Overview of the Market
1.2 Scope of Report
1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

3.1 Data Mining
3.2 Validation
3.3 Primary Interviews
3.4 List of Data Sources

4 GLOBAL NATURAL LANGUAGE PROCESSING MARKET OUTLOOK

4.1 Overview
4.2 Market Dynamics
- 4.2.1 Drivers
- 4.2.2 Restraints
- 4.2.3 Opportunities
4.3 Porters Five Force Model
4.4 Value Chain Analysis

5 GLOBAL NATURAL LANGUAGE PROCESSING MARKET, BY TYPE

5.1 Overview
5.2 Statistical NLP
5.3 Rule Based NLP
5.4 Hybrid NLP

6 GLOBAL NATURAL LANGUAGE PROCESSING MARKET, BY DEPLOYMENT MODE

6.1 Overview
6.2 Private Cloud
6.3 Public Cloud
6.4 Hybrid Cloud

7 GLOBAL NATURAL LANGUAGE PROCESSING MARKET, BY APPLICATION

7.1 Overview
7.2 Information Extraction
7.3 Machine translation
7.4 Language Translation
7.5 Question Answering
7.6 Speech Recognition
7.7 Text Summarization
7.8 Report generation
7.9 Others

8 GLOBAL NATURAL LANGUAGE PROCESSING MARKET, BY END USERS

8.1 Overview
8.2 Healthcare
8.3 Banking, Financial Services, & Insurance (BFSI)
8.4 Consumer Goods
8.5 Research & Education
8.6 E-Commerce
8.7 Electronics
8.8 Manufacturing
8.9 Media & Entertainment

9 GLOBAL NATURAL LANGUAGE PROCESSING MARKET, BY GEOGRAPHY

9.1 Overview
9.2 North America
- 9.2.1 U.S.
- 9.2.2 Canada
- 9.2.3 Mexico
9.3 Europe
- 9.3.1 Germany
- 9.3.2 U.K.
- 9.3.3 France
- 9.3.4 Rest of Europe
9.4 Asia Pacific
- 9.4.1 China
- 9.4.2 Japan
- 9.4.3 India
- 9.4.4 Rest of Asia Pacific
9.5 Rest of the World
- 9.5.1 Latin America
- 9.5.2 Middle East and Africa

10 GLOBAL NATURAL LANGUAGE PROCESSING MARKET COMPETITIVE LANDSCAPE

10.1 Overview
10.2 Company Market Ranking
10.3 Key Development Strategies

11 COMPANY PROFILES

11.1 3M
- 11.1.1 Overview
- 11.1.2 Financial Performance
- 11.1.3 Product Outlook
- 11.1.4 Key Developments
11.2 Apple Inc.
- 11.2.1 Overview
- 11.2.2 Financial Performance
- 11.2.3 Product Outlook
- 11.2.4 Key Developments
11.3 Amazon Web Services Inc.
- 11.3.1 Overview
- 11.3.2 Financial Performance
- 11.3.3 Product Outlook
- 11.3.4 Key Developments
11.4 Baidu Inc.
- 11.4.1 Overview
- 11.4.2 Financial Performance
- 11.4.3 Product Outlook
- 11.4.4 Key Developments
11.5 Crayon Data
- 11.5.1 Overview
- 11.5.2 Financial Performance
- 11.5.3 Product Outlook
- 11.5.4 Key Developments
11.6 Google LLC
- 11.6.1 Overview
- 11.6.2 Financial Performance
- 11.6.3 Product Outlook
- 11.6.4 Key Developments
11.7 Health Fidelity
- 11.7.1 Overview
- 11.7.2 Financial Performance
- 11.7.3 Product Outlook
- 11.7.4 Key Developments
11.8 IBM Corporation
- 11.8.1 Overview
- 11.8.2 Financial Performance
- 11.8.3 Product Outlook
- 11.8.4 Key Developments
11.9 Inbenta
- 11.9.1 Overview
- 11.9.2 Financial Performance
- 11.9.3 Product Outlook
- 11.9.4 Key Developments
11.10 IQVIA
- 11.10.1 Overview
- 11.10.2 Financial Performance
- 11.10.3 Product Outlook
- 11.10.4 Key Developments