Paradigm Shift In Natural Language Processing
Credit: Beijing Zhongke Journal Publising Co. Ltd.
In the scope of this paper, a paradigm is a general modeling framework or a distinct set of methodologies to solve a class of tasks. For instance, sequence labeling is a mainstream paradigm for named entity recognition (NER). Different paradigms usually require different formats of input and output, and therefore highly depend on the annotation of the tasks. In the past years, modeling for most NLP tasks has converged to several mainstream paradigms, as summarized in this paper,
Class, Matching, SeqLab, MRC, Seq2Seq, Seq2ASeq, and (M)LM.
Though the paradigm for many tasks has converged and dominated for a long time, recent work has shown that models under some paradigms also generalize well on tasks with other paradigms. For example, the MRC and Seq2Seq paradigms can also achieve state-of-the-art performance on NER tasks, which were previously formalized in the sequence labeling (SeqLab) paradigm. Such methods typically first convert the form of the dataset to the form required by the new paradigm, and then use the model under the new paradigm to solve the task. In recent years, similar methods that reformulate a Natural Language Processing (NLP) task as another one has achieved great success and gained increasing attention in the community. After the emergence of pre-trained language models (PTMs), paradigm shifts have been observed in an increasing number of tasks. Combined with the power of these PTMs, some paradigms have shown great potential to unify diverse NLP tasks. One of these potential unified paradigms, (M)LM (also referred to as prompt-based tuning), has made rapid progress recently, making it possible to employ a single PTM as the universal solver for various understanding and generation tasks.
Despite their success, these paradigm shifts scattering in various NLP tasks have not been systematically reviewed and analyzed. In this paper, researchers attempt to summarize recent advances and trends in this line of research, namely paradigm shift or paradigm transfer.
This paper is organized as follows. Section 2 gives formal definitions of the seven paradigms, and introduces their representative tasks and instance models. Section 3 shows recent paradigm shifts that happened in different NLP tasks. Section 4 discusses the designs and challenges of several highlighted paradigms that have great potential to unify most existing NLP tasks. Section 5 concludes with a brief discussion of recent trends and future directions.
Section 2 briefly introduces the following seven paradigms that are widely used in NLP tasks and their corresponding tasks and models. The seven paradigms are Class, Matching, SeqLab, MRC, Seq2Seq, Seq2ASeq, and (M)LM. These paradigms have demonstrated strong dominance in many mainstream NLP tasks.
In section 3, researchers review the paradigm shifts that occur in different NLP tasks: text classification, Natural Language inference, named entity recognition, aspect-based sentiment analysis, relation exaction, text summarization, and parsing. Researchers also propose the trends of paradigm shift. They find that: 1) The frequency of paradigm shifts has been increasing in recent years, especially after the emergence of pre-trained language models (PTMs). Therefore, to fully utilize the power of these PTMs, a better way is to reformulate various NLP tasks into the paradigms that PTMs are good at. 2) More and more NLP tasks have shifted from traditional paradigms to paradigms that are more general and flexible.
Section 4 discusses the following general paradigms that have the potential to unify diverse NLP
tasks: (M)LM, Matching, MRC, and Seq2Seq. Part one includes prompt, verbalizer, parameter-efficient prompt tuning in (M)LM; Part two includes domain adaptation, label descriptions, comparison with prompt-based learning in Matching; Part three consists of the brief introduction of MRC and comparison with prompt-based learning; The last part consists of brief introduction of Seq2Seq and comparison with other paradigms.
Section 5 is the conclusion of this paper. Recently, prompt-based tuning, which is to formulate some NLP tasks into an (M)LM task, has exploded in popularity. They can achieve considerable performance with much less training data. In contrast, other potential unified paradigms, i.E., Matching, MRC, and Seq2Seq, are under-explored in the context of pre-training. One of the main reasons is that these paradigms require large-scale annotated data to conduct pre-training, especially Seq2Seq is notorious for being data-hungry. Nevertheless, these paradigms have their advantages over (M)LM: Matching requires less engineering, MRC is more interpretable, and Seq2Seq is more flexible to handle complicated tasks. Besides, by combining with self-supervised pre-training, or further pre-training on annotated data with existing language model as initialization, these paradigms can achieve competitive performance or even better performance than (M)LM. Therefore, researchers argue that more attention is needed for the exploration of more powerful entailment, MRC, or Seq2Seq models through pre-training or other alternative techniques.
See the article:
Paradigm Shift in Natural Language Processing
http://doi.Org/10.1007/s11633-022-1331-6
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Natural Language Processing And Recognition Market Growth Strategies 2023: Top Companies, Business Demand And Future Predictions Till 2030
The Research Report on Natural Language Processing and Recognition Market [116 Pages] offers thorough perspective on industry performance, latest key trends and comprehensive exploration of Industry segments by Type [On-premises, Cloud], Applications [BFSI, IT and Telecom,, Retail and Ecommerce, Healthcare and Life Sciences, Transportation and Logistics, Government and Public Sector, Energy and Utilities, Manufacturing, Others] and Regions. The report presents concise aspects on key dynamics with market growth rate, size, trade, and insights into key players. It highlights the convergence of market trends, business tactics, and the competitive environment. This report goes beyond conventional analyses by providing both qualitative and quantitative perspectives through SWOT and PESTLE evaluations. Through meticulous research and thorough analysis, the report aims to offer valuable insights to stakeholders, vendors, and various participants within the industry.
In our latest research report, we highlight the rapid growth of the global Natural Language Processing and Recognition market and provide detailed insights into the projected market size, share, and revenue estimations up to 2030.Ask for Sample Report
Who is the Largest Player of Natural Language Processing and Recognition Market worldwide?
NetBase Solutions Inc. Micro Focus International PLC (HPE) Microsoft Corporation App Orchid Inc. Adobe Inc. Inbenta Technologies Inc. Babylon Healthcare Services Limited Rasa Technologies GmbH SAS Institute Inc. Genpact Limited Salesforce.Com Inc. Veritone Inc. NVIDIA Corporation Amazon Web Services Inc. Google Inc. Intel Corporation Klevu Oy IBM Corporation SAP SE Verint System Inc.
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What is Market Insights and Analysis?
The Natural Language Processing and Recognition market has witnessed a growth from USD million to USD million from 2017 to 2022. With a CAGR, this market is estimated to reach USD million in 2029.
The report focuses on the Natural Language Processing and Recognition market size, segment size (mainly covering product type, application, and geography), competitor landscape, recent status, and development trends. Furthermore, the report provides strategies for companies to overcome threats posed by COVID-19.
Technological innovation and advancement will further optimize the performance of the product, enabling it to acquire a wider range of applications in the downstream market. Moreover, customer preference analysis, market dynamics (drivers, restraints, opportunities), new product release, impact of COVID-19, regional conflicts and carbon neutrality provide crucial information for us to take a deep dive into the Natural Language Processing and Recognition market.
The competitive landscape analysis encompasses a thorough examination of key players operating in the market. It assesses their market presence, product offerings, strategic initiatives, and growth trajectories. This analysis empowers businesses with valuable insights to make informed decisions, adapt to market trends, and devise effective strategies to maintain a competitive edge in the dynamic industry landscape.
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What are the factors driving the growth of the Natural Language Processing and Recognition Market?
Growing demand for below applications around the world has had a direct impact on the growth of the Natural Language Processing and Recognition
BFSI, IT and Telecom,
Retail and Ecommerce
Healthcare and Life Sciences
Transportation and Logistics
Government and Public Sector
Energy and Utilities
Manufacturing
Others
What are the types of Natural Language Processing and Recognition available in the Market?
Based on Product Types the Market is categorized into Below types that held the largest Natural Language Processing and Recognition market share In 2023.
On-premises
Cloud
Regional Outlook:
North America (United States, Canada and Mexico)
Europe (Germany, UK, France, Italy, Russia and Turkey etc.)
Asia-Pacific (China, Japan, Korea, India, Australia, Indonesia, Thailand, Philippines, Malaysia and Vietnam)
South America (Brazil, Argentina, Columbia etc.)
Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria and South Africa)
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Following Key Questions Covered in this Report:
What is the Current Market Size and Growth Rate of the Natural Language Processing and Recognition Market?
What are the Key Trends and Developments Shaping the Natural Language Processing and Recognition Market?
What are the Main Drivers and Restraints Affecting the Growth of the Natural Language Processing and Recognition Market?
How is the Natural Language Processing and Recognition Market Segmented by Manufacturers, Types, Applications, and Regions?
Who are the Major Players in the Natural Language Processing and Recognition Market and What are Their Strategies?
What is the Competitive Landscape and Market Share of Different Companies?
What are the Future Growth Prospects and Opportunities in the Natural Language Processing and Recognition Market?
What are the Industry Challenges and Potential Mitigation Strategies?
How is Consumer Behavior Impacting Demand Patterns in the Natural Language Processing and Recognition Market?
What is the Impact of Regulatory Policies on the Natural Language Processing and Recognition Market?
What are the Technological Innovations and Advancements in the Natural Language Processing and Recognition Industry?
What is the Forecasted Market Growth Rate and Potential Size in the Coming Years?
What are the Key Market Entry Barriers and How Can They Be Overcome?
What is the Impact of External Factors, such as COVID-19, on the Natural Language Processing and Recognition Market?
What are the Evolving Customer Preferences and Their Impact on the Market?
Covid-19 Impact on Natural Language Processing and Recognition Market:
The unprecedented outbreak of the Covid-19 pandemic has reverberated across industries worldwide, ushering in a period of profound transformation. The landscape of businesses and markets has been reshaped as supply chains were disrupted, consumer behaviors shifted, and economies faced unforeseen challenges. Comprehensive research on the Covid-19 impact on various industries has become imperative to understand the extent of its influence, ranging from disruptions in production and distribution to changes in demand patterns and workforce dynamics. This research delves into the multifaceted repercussions, offering insights into strategies for resilience, adaptation, and recovery. It sheds light on the evolving paradigms within industries, providing a roadmap for stakeholders to navigate these uncertain times with informed decisions and strategic responses.
Key inclusions of the Natural Language Processing and Recognition market report:
A detailed impact analysis of COVID-19 on the Natural Language Processing and Recognition market.
In-depth statistical analysis of market size, sales volume, and revenue, segmented by product type, application, and geography.
Comprehensive coverage of major market trends, including drivers, challenges, and opportunities.
Identification and analysis of growth opportunities for businesses operating in the Natural Language Processing and Recognition market.
Accurate and up-to-date figures showcasing the market growth rate and projected growth trends.
A thorough examination of the advantages and disadvantages of both direct and indirect sales channels in the Natural Language Processing and Recognition market.
Insights into the key players in the industry, including traders, distributors, and dealers, and their impact on the market.
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Detailed TOC of Natural Language Processing and Recognition Market Research Report:
1 Natural Language Processing and Recognition Market Overview
1.1 Product Overview and Scope of Natural Language Processing and Recognition Market
1.2 Natural Language Processing and Recognition Market Segment by Type
1.3 Global Natural Language Processing and Recognition Market Segment by Application
1.4 Global Natural Language Processing and Recognition Market, Region Wise (2017-2029)
1.5 Global Market Size (Revenue) of Natural Language Processing and Recognition (2017-2029)
1.5.1 Global Natural Language Processing and Recognition Market Revenue Status and Outlook (2017-2029)
1.5.2 Global Natural Language Processing and Recognition Market Sales Status and Outlook (2017-2029)
1.6 Influence of Regional Conflicts on the Natural Language Processing and Recognition Industry
1.7 Impact of Carbon Neutrality on the Natural Language Processing and Recognition Industry
2 Natural Language Processing and Recognition Market Upstream and Downstream Analysis
2.1 Natural Language Processing and Recognition Industrial Chain Analysis
2.2 Key Raw Materials Suppliers and Price Analysis
2.3 Key Raw Materials Supply and Demand Analysis
2.4 Market Concentration Rate of Raw Materials
2.5 Manufacturing Process Analysis
2.6 Manufacturing Cost Structure Analysis
2.6.1 Labor Cost Analysis
2.6.2 Energy Costs Analysis
2.6.3 RandD Costs Analysis
2.7 Major Downstream Buyers of Natural Language Processing and Recognition Analysis
2.8 Impact of COVID-19 on the Industry Upstream and Downstream
3 Players Profiles
4 Global Natural Language Processing and Recognition Market Landscape by Player
4.1 Global Natural Language Processing and Recognition Sales and Share by Player (2017-2022)
4.2 Global Natural Language Processing and Recognition Revenue and Market Share by Player (2017-2022)
4.3 Global Natural Language Processing and Recognition Average Price by Player (2017-2022)
4.4 Global Natural Language Processing and Recognition Gross Margin by Player (2017-2022)
4.5 Natural Language Processing and Recognition Market Competitive Situation and Trends
4.5.1 Natural Language Processing and Recognition Market Concentration Rate
4.5.2 Natural Language Processing and Recognition Market Share of Top 3 and Top 6 Players
4.5.3 Mergers and Acquisitions, Expansion
5 Global Natural Language Processing and Recognition Sales, Revenue, Price Trend by Type
5.1 Global Natural Language Processing and Recognition Sales and Market Share by Type (2017-2022)
5.2 Global Natural Language Processing and Recognition Revenue and Market Share by Type (2017-2022)
5.3 Global Natural Language Processing and Recognition Price by Type (2017-2022)
5.4 Global Natural Language Processing and Recognition Sales, Revenue and Growth Rate by Type (2017-2022)
6 Global Natural Language Processing and Recognition Market Analysis by Application
6.1 Global Natural Language Processing and Recognition Consumption and Market Share by Application (2017-2022)
6.2 Global Natural Language Processing and Recognition Consumption Revenue and Market Share by Application (2017-2022)
6.3 Global Natural Language Processing and Recognition Consumption and Growth Rate by Application (2017-2022)
7 Global Natural Language Processing and Recognition Sales and Revenue Region Wise (2017-2022)
7.1 Global Natural Language Processing and Recognition Sales and Market Share, Region Wise (2017-2022)
7.2 Global Natural Language Processing and Recognition Revenue and Market Share, Region Wise (2017-2022)
7.3 Global Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.4 United States Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.5 Europe Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.6 China Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.7 Japan Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.8 India Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.9 Southeast Asia Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.10 Latin America Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
7.11 Middle East and Africa Natural Language Processing and Recognition Sales, Revenue, Price and Gross Margin (2017-2022)
8 Global Natural Language Processing and Recognition Market Forecast (2022-2029)
8.1 Global Natural Language Processing and Recognition Sales, Revenue Forecast (2022-2029)
8.2 Global Natural Language Processing and Recognition Sales and Revenue Forecast, Region Wise (2022-2029)
8.3 Global Natural Language Processing and Recognition Sales, Revenue and Price Forecast by Type (2022-2029)
8.4 Global Natural Language Processing and Recognition Consumption Forecast by Application (2022-2029)
8.5 Natural Language Processing and Recognition Market Forecast Under COVID-19
9 Industry Outlook
9.1 Natural Language Processing and Recognition Market Drivers Analysis
9.2 Natural Language Processing and Recognition Market Restraints and Challenges
9.3 Natural Language Processing and Recognition Market Opportunities Analysis
9.4 Emerging Market Trends
9.5 Natural Language Processing and Recognition Industry Technology Status and Trends
9.6 News of Product Release
9.7 Consumer Preference Analysis
9.8 Natural Language Processing and Recognition Industry Development Trends under COVID-19 Outbreak
10 Research Findings and Conclusion
11 Appendix
11.1 Methodology
11.2 Research Data Source
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