Context

The dataset appears to be a collection of NLP research papers, with the full text available in the "article" column, abstract summaries in the "abstract" column, and information about different sections in the "section_names" column. Researchers and practitioners in the field of natural language processing can use this dataset for various tasks, including text summarization, document classification, and analysis of research paper structures.

Data Fields

Here's a short description of the Natural Language Processing Research Papers dataset: 1. Article: This column likely contains the full text or content of the research papers related to Natural Language Processing (NLP). Each entry in this column represents the entire body of a specific research article. 2. Abstract: This column is likely to contain the abstracts of the NLP research papers. The abstract provides a concise summary of the paper, highlighting its key objectives, methods, and findings. 3. Section Names: This column probably contains information about the section headings within each research paper. It could include the names or titles of different sections such as Introduction, Methodology, Results, Conclusion, etc. This information can be useful for structuring and organizing the content of the research papers.

File Description

Content Overview: The dataset is valuable for researchers, students, and practitioners in the field of Natural Language Processing. File format: This file is csv format.

Dataset

This dataset was created by AbiyuG

Released under CC BY-NC-SA 4.0

Contents

Snippets database has sound / audio / sonic recordings across all kinds of venues (restaurants, bars, arenas, churches, movie theaters, retail stores, factories, parks, libraries, gyms, hotels, offices, factories and many more) and variance in noise levels (Quiet, Moderate, Loud, Very Loud), noise types and acoustic environments with valuable metadata.

This is valuable for any audio-based software product/company to run/test its algorithm against various acoustic environments including:

1. Hearing aid companies wanting to test their software's ability to identify or separate certain sounds and background noise and mitigate them

2. Audio or Video Conferencing platforms that want to be able to identify a user's location (i.e. user joins call from a coffee shop and platform has ability to identify and mitigate such sounds for better audio

3. Other audio-based use cases

Comprehensive Portuguese language datasets with linguistic annotations, including headwords, definitions, word senses, usage examples, part-of-speech (POS) tags, semantic metadata, and contextual usage details. Perfect for powering dictionary platforms, NLP, AI models, and translation systems.

Our Portuguese language datasets are carefully compiled and annotated by language and linguistic experts. The below datasets in Portuguese are available for license:

1. Portuguese Monolingual Dictionary Data
2. Portuguese Bilingual Dictionary Data

Key Features (approximate numbers):

1. Portuguese Monolingual Dictionary Data

Our Portuguese monolingual covers both EU and LATAM varieties, featuring clear definitions and examples, a large volume of headwords, and comprehensive coverage of the Portuguese language.

• Words:143,600
• Senses: 285,500
• Example sentences: 69,300
• Format: XML format
• Delivery: Email (link-based file sharing)

1. Portuguese Bilingual Dictionary Data

The bilingual data provides translations in both directions, from English to Portuguese and from Portuguese to English. It is annually reviewed and updated by our in-house team of language experts. Offers comprehensive coverage of the language, providing a substantial volume of translated words of excellent quality that span both EU and LATAM Portuguese varieties.

• Translations: 300,000
• Senses: 158,000
• Example translations: 117,800
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include Natural Language Processing (NLP) applications, TTS, dictionary display tools, games, translations, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals.

About the sample:

The samples offer a brief overview of one or two language datasets (monolingual or/and bilingual dictionary data). To help you explore the structure and features of our dataset, we provide a sample in CSV format for preview purposes only.

If you need the complete original sample or more details about any dataset, please contact us (Growth.OL@oup.com) to request access or further information

1. Overview
2. Unsupervised Learning: For the training data required in unsupervised learning, Nexdata delivers data collection and cleaning services for both single-modal and cross-modal data. We provide Large Language Model(LLM) Data cleaning and personnel support services based on the specific data types and characteristics of the client's domain.

-SFT: Nexdata assists clients in generating high-quality supervised fine-tuning data for model optimization through prompts and outputs annotation.

-Red teaming: Nexdata helps clients train and validate models through drafting various adversarial attacks, such as exploratory or potentially harmful questions. Our red team capabilities help clients identify problems in their models related to hallucinations, harmful content, false information, discrimination, language bias and etc.

-RLHF: Nexdata assist clients in manually ranking multiple outputs generated by the SFT-trained model according to the rules provided by the client, or provide multi-factor scoring. By training annotators to align with values and utilizing a multi-person fitting approach, the quality of feedback can be improved.

1. Our Capacity -Global Resources: Global resources covering hundreds of languages worldwide

-Compliance: All the Large Language Model(LLM) Data is collected with proper authorization

-Quality: Multiple rounds of quality inspections ensures high quality data output

-Secure Implementation: NDA is signed to gurantee secure implementation and data is destroyed upon delivery.

-Efficency: Our platform supports human-machine interaction and semi-automatic labeling, increasing labeling efficiency by more than 30% per annotator. It has successfully been applied to nearly 5,000 projects.

3.About Nexdata Nexdata is equipped with professional data collection devices, tools and environments, as well as experienced project managers in data collection and quality control, so that we can meet the Large Language Model(LLM) Data collection requirements in various scenarios and types. We have global data processing centers and more than 20,000 professional annotators, supporting on-demand Large Language Model(LLM) Data annotation services, such as speech, image, video, point cloud and Natural Language Processing (NLP) Data, etc. Please visit us at https://www.nexdata.ai/?source=Datarade

Natural Language Processing Dataset for research into a complex keyword model. Data is split into two models one for colors and another for commands. These keywords were all trained on one voice on the Arduino Nano 33 BLE sense chip's microphone. The data itself exists in both .wav and .json formats and can be imported to Edge Impulse for proper use or the .wav files can be used for model training outside of Edge Impulse formats. The dataset itself also contains a test and train split and each file has an identifier for its specific label.

The data for the smoking challenge consisted exclusively of discharge summaries from Partners HealthCare which were preprocessed and converted into XML format, and separated into training and test sets. I2B2 is a data warehouse containing clinical data on over 150k patients, including outpatient DX, lab results, medications, and inpatient procedures. ETL processes authored to pull data from EMR and finance systems Institutional review boards of Partners HealthCare approved the challenge and the data preparation process. The data were annotated by pulmonologists and classified patients into Past Smokers, Current Smokers, Smokers, Non-smokers, and unknown. Second-hand smokers were considered non-smokers. Other institutions involved include Massachusetts Institute of Technology, and the State University of New York at Albany. i2b2 is a passionate advocate for the potential of existing clinical information to yield insights that can directly impact healthcare improvement. In our many use cases (Driving Biology Projects) it has become increasingly obvious that the value locked in unstructured text is essential to the success of our mission. In order to enhance the ability of natural language processing (NLP) tools to prise increasingly fine grained information from clinical records, i2b2 has previously provided sets of fully deidentified notes from the Research Patient Data Repository at Partners HealthCare for a series of NLP Challenges organized by Dr. Ozlem Uzuner. We are pleased to now make those notes available to the community for general research purposes. At this time we are releasing the notes (~1,000) from the first i2b2 Challenge as i2b2 NLP Research Data Set #1. A similar set of notes from the Second i2b2 Challenge will be released on the one year anniversary of that Challenge (November, 2010).

Round 6 Test DatasetThis is the test data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform text sentiment classification on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 480 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Natural Language Processing (NLP) in Healthcare Market Outlook

As of 2023, the Natural Language Processing (NLP) in Healthcare market is projected to reach a valuation of approximately $3.5 billion, with a growth rate that is anticipated to exceed 20% CAGR from 2024 to 2032. This exponential growth is driven by the increasing adoption of artificial intelligence in healthcare, particularly for enhancing efficiency and accuracy in clinical documentation, patient monitoring, and drug development.

One of the primary growth factors fueling the NLP in Healthcare market is the ever-increasing volume of healthcare data. With the proliferation of electronic health records (EHRs), medical literature, and clinical trial data, healthcare providers are inundated with vast amounts of unstructured information. NLP technologies facilitate the conversion of this unstructured data into actionable insights, enabling healthcare professionals to make informed decisions swiftly and accurately. Additionally, the advancement of machine learning algorithms and big data analytics aids in refining NLP capabilities, further accelerating market growth.

Another significant driver of market growth is the rising demand for personalized medicine. As healthcare moves towards a more patient-centric approach, there is a growing need for technologies that can analyze patient data comprehensively to provide tailored treatment plans. NLP systems play a crucial role in this by analyzing patient histories, genetic information, and lifestyle factors to recommend personalized treatments. This not only improves patient outcomes but also enhances patient satisfaction and adherence to treatment protocols.

The increasing prevalence of chronic diseases such as diabetes, cardiovascular diseases, and cancer is also contributing to the growth of the NLP in Healthcare market. Managing chronic conditions requires continuous monitoring and regular adjustments to treatment plans, which can be efficiently handled by NLP-driven systems. These systems can analyze patient data in real-time, alert healthcare providers to any anomalies, and suggest timely interventions. This reduces the burden on healthcare systems and improves the quality of care provided to patients.

From a regional perspective, North America holds a dominant share in the NLP in Healthcare market, driven by advanced healthcare infrastructure, high adoption rates of cutting-edge technologies, and significant investments in R&D. Europe is also anticipated to witness substantial growth, supported by favorable government policies and increasing awareness about the benefits of AI in healthcare. The Asia Pacific region is emerging as a lucrative market due to the rapid development of healthcare facilities, growing patient population, and increasing investments in healthcare technologies. Latin America and the Middle East & Africa are also expected to show steady growth, although at a relatively slower pace due to varying levels of technological adoption and healthcare infrastructure.

Component Analysis

In the NLP in Healthcare market, segmentation by component includes software, hardware, and services. The software segment is expected to dominate the market, owing to the continuous advancements in NLP algorithms and the increasing integration of AI-driven software in healthcare systems. NLP software solutions are essential for tasks such as clinical documentation, medical research, and patient monitoring. These solutions help in extracting valuable insights from unstructured data, thereby enhancing decision-making processes and operational efficiency.

The hardware segment, although smaller compared to software, plays a critical role in supporting NLP applications. This includes servers, data storage devices, and other IT infrastructure necessary for running complex NLP algorithms. As the demand for real-time data processing and analysis grows, there is a corresponding increase in the need for robust and scalable hardware solutions. Investments in high-performance computing systems and cloud-based infrastructure are driving the growth of this segment.

The services segment is also gaining traction, encompassing consulting, implementation, and maintenance services. As healthcare organizations increasingly adopt NLP technologies, there is a growing need for expertise in deploying and managing these solutions. Service providers offer valuable support in customizing NLP applications to meet specific healthcare needs, integrating these solutions with existing systems, and ensuring their smooth operation. Additionally,

hyletuan/cotai-nlp-data dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains the DOIs of the corpus, used for the natural language processing analysis described in the article of the same title. The DOIs all point to articles published in the Microscopy and Microanalysis conference proceeding, spanning 2002 through 2019.

The Natural Language Processing (NLP) market in healthcare and life sciences is experiencing rapid growth, projected to reach $2177.2 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 17.1%. This robust expansion is driven by several key factors. The increasing volume of unstructured clinical data, including electronic health records (EHRs) and physician notes, necessitates efficient and accurate processing. NLP solutions offer automated data extraction, summarization, and analysis, improving operational efficiency and reducing manual workload. Furthermore, the rising adoption of telehealth and remote patient monitoring generates substantial data requiring sophisticated analysis, creating significant demand for NLP technologies. Applications like computer-assisted coding (CAC) are streamlining administrative processes, accelerating reimbursement cycles, and minimizing human error. Advances in machine translation capabilities are facilitating global collaboration in research and patient care, further fueling market growth. Regulatory compliance mandates and the focus on improving patient outcomes are additional factors stimulating adoption. Market segmentation reveals a diverse landscape. Electronic Health Records (EHR) processing currently holds a dominant market share among applications, followed by Computer-Assisted Coding (CAC). Within types, machine translation and information extraction are key segments exhibiting strong growth. North America, particularly the United States, is expected to remain the largest regional market, driven by advanced healthcare infrastructure and high technology adoption. However, other regions like Asia Pacific are witnessing significant growth due to increasing healthcare investment and the growing adoption of digital health technologies. While challenges remain, including data privacy concerns and the need for robust data security protocols, the overall market outlook for NLP in healthcare and life sciences remains overwhelmingly positive, suggesting sustained growth and innovation throughout the forecast period (2025-2033).

WORKBank (AI Agent Worker Outlook and Readiness Knowledge Bank) is a database that captures worker desire and technological capability of AI agents for occupational tasks. The current version of WORKBank includes preferences from 1,500 U.S. domain workers and capability assessments from AI experts, covering over 844 tasks across 104 occupations collected between January and May 2025. This database stems from our project detailed in Future of Work with AI Agents: Auditing Automation and… See the full description on the dataset page: https://huggingface.co/datasets/SALT-NLP/WORKBank.

Dataset

This dataset was created by Rafael Sandroni

Contents

The Natural Language Processing (NLP) solutions market is experiencing robust growth, driven by the increasing adoption of AI-powered applications across various sectors. The market's expansion is fueled by the rising volume of unstructured data, the need for efficient data analysis and automation, and the growing demand for personalized customer experiences. Technological advancements, such as deep learning and improved algorithms, are enhancing NLP capabilities, enabling more accurate language understanding and generation. Key applications include chatbots, virtual assistants, sentiment analysis, machine translation, and text summarization. While market size data is not explicitly provided, based on the presence of major players like IBM, Google, and Microsoft, and considering the rapid growth of AI, we can estimate the 2025 market size to be around $15 billion. Assuming a conservative CAGR (Compound Annual Growth Rate) of 20% (a reasonable estimate given the current market dynamics), the market is projected to reach approximately $40 billion by 2033. The market is segmented across various industries, including healthcare, finance, retail, and customer service. Healthcare's adoption of NLP for medical record analysis and patient engagement is a significant growth driver. Financial institutions leverage NLP for fraud detection, risk management, and regulatory compliance. Retail businesses utilize NLP for personalized marketing and customer service automation. While there are restraining factors such as data privacy concerns and the need for high-quality training data, the overall market outlook remains positive. The competitive landscape is characterized by both large technology companies and specialized NLP solution providers, fostering innovation and competition. This leads to continuous improvement in accuracy, efficiency, and the affordability of NLP solutions, further accelerating market growth. The forecast period of 2025-2033 offers substantial opportunities for businesses to capitalize on this rapidly evolving technology.

Global natural language processing (NLP) market worth at USD 25.90 Billion in 2024, is expected to surpass USD 206.32 Billion by 2034, with a CAGR of 23.06%.

The global Healthcare Natural Language Processing (NLP) market, valued at $885.1 million in 2025, is projected to experience steady growth, driven by a Compound Annual Growth Rate (CAGR) of 3.4% from 2025 to 2033. This expansion is fueled by several key factors. The increasing volume of unstructured healthcare data, including electronic health records (EHRs), clinical notes, and research papers, necessitates efficient and accurate analysis for improved patient care and research. NLP technologies offer a powerful solution by automating tasks like data extraction, summarization, and sentiment analysis, freeing up clinicians' time and enabling faster, more informed decision-making. Furthermore, advancements in deep learning and machine learning algorithms are enhancing the accuracy and capabilities of NLP systems, leading to broader adoption across various healthcare applications such as medical imaging analysis, drug discovery, and personalized medicine. The growing emphasis on value-based care and the need for improved healthcare efficiency further propel market growth. However, challenges remain. Data privacy and security concerns surrounding sensitive patient information are significant hurdles. Ensuring compliance with regulations like HIPAA is crucial for widespread adoption. The heterogeneity of healthcare data formats and the need for robust data preprocessing also present obstacles. Additionally, the high cost of implementing and maintaining NLP systems and the lack of skilled professionals to manage these technologies can limit market penetration, especially in smaller healthcare settings. Despite these restraints, the long-term outlook for the Healthcare NLP market remains positive, with continuous technological advancements and increasing awareness of its benefits driving market expansion across various healthcare sub-sectors. Key players like NLP Technologies, NEC, Apple, Microsoft, Dolby, IBM, NetBase, SAS, Verint Systems, Linguamatics, and Artificial Solutions are actively shaping the market landscape through continuous innovation and strategic partnerships.

The Natural Language Processing (NLP) technology market is experiencing robust growth, projected to reach $2271.9 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 2.4% from 2019 to 2033. This growth is fueled by several key drivers. The increasing adoption of AI-powered solutions across diverse industries, including healthcare, finance, and customer service, is significantly boosting demand for NLP capabilities. Advancements in deep learning and machine learning algorithms are leading to more accurate and efficient NLP systems, further fueling market expansion. The growing availability of large, high-quality datasets for training NLP models is also a significant factor. Furthermore, the rising need for automated customer service and improved data analysis is driving the integration of NLP technologies into various business processes, generating significant market opportunities. The market is segmented into Natural Language Understanding (NLU) and Natural Language Generation (NLG), with applications spanning text retrieval, machine translation, and information extraction. Major players such as Google, Amazon Web Services, IBM, and Microsoft are actively investing in research and development, leading to continuous innovation and enhancing the market's overall competitiveness. While the market exhibits considerable growth potential, certain challenges remain. The complexity of natural language and the inherent ambiguity in human communication pose significant technical hurdles. Data privacy concerns and the ethical implications of using NLP technologies require careful consideration. Furthermore, the high cost of developing and implementing advanced NLP solutions can limit adoption, particularly among smaller businesses. Despite these challenges, the long-term outlook for the NLP market remains positive, driven by continuous technological advancements and the increasing reliance on data-driven decision-making across industries. The market's segmentation by application and region provides valuable insights for strategic planning and investment decisions. North America currently holds a significant market share, but the Asia-Pacific region is expected to demonstrate substantial growth in the coming years.

Derived from over 150 years of lexical research, these comprehensive textual and audio data, focused on American English, provide linguistically annotated data. Ideal for NLP applications, LLM training and/or fine-tuning, as well as educational and game apps.

One of our flagship datasets, the American English data is expertly curated and linguistically annotated by professionals, with annual updates to ensure accuracy and relevance. The below datasets in American English are available for license:

1. American English Monolingual Dictionary Data
2. American English Synonyms and Antonyms Data
3. American English Pronunciations with Audio

Key Features (approximate numbers):

1. American English Monolingual Dictionary Data

Our American English Monolingual Dictionary Data is the foremost authority on American English, including detailed tagging and labelling covering parts of speech (POS), grammar, region, register, and subject, providing rich linguistic information. Additionally, all grammar and usage information is present to ensure relevance and accuracy.

• Headwords: 140,000
• Senses: 222,000
• Sentence examples: 140,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Synonyms and Antonyms Data

The American English Synonyms and Antonyms Dataset is a leading resource offering comprehensive, up-to-date coverage of word relationships in contemporary American English. It includes rich linguistic details such as precise definitions and part-of-speech (POS) tags, making it an essential asset for developing AI systems and language technologies that require deep semantic understanding.

• Synonyms: 600,000
• Antonyms: 22,000
• Format: XML and JSON format
• Delivery: Email (link-based file sharing) and REST API
• Updated frequency: annually

1. American English Pronunciations with Audio (word-level)

This dataset provides IPA transcriptions and clean audio data in contemporary American English. It includes syllabified transcriptions, variant spellings, POS tags, and pronunciation group identifiers. The audio files are supplied separately and linked where available for seamless integration - perfect for teams building TTS systems, ASR models, and pronunciation engines.

• Transcriptions (IPA): 250,000
• Audio files: 180,000
• Format: XLSX (for transcriptions), MP3 and WAV (audio files)
• Updated frequency: annually

Use Cases:

We consistently work with our clients on new use cases as language technology continues to evolve. These include NLP applications, TTS, dictionary display tools, games, translation machine, AI training and fine-tuning, word embedding, and word sense disambiguation (WSD).

If you have a specific use case in mind that isn't listed here, we’d be happy to explore it with you. Don’t hesitate to get in touch with us at Growth.OL@oup.com to start the conversation.

Pricing:

Oxford Languages offers flexible pricing based on use case and delivery format. Our datasets are licensed via term-based IP agreements and tiered pricing for API-delivered data. Whether you’re integrating into a product, training an LLM, or building custom NLP solutions, we tailor licensing to your specific needs.

Contact our team or email us at Growth.OL@oup.com to explore pricing options and discover how our language data can support your goals. Please note that some datasets may have rights restrictions. Contact us for more information.

About the sample:

To help you explore the structure and features of our dataset on this platform, we provide a sample in CSV and/or JSON formats for one of the presented datasets, for preview purposes only, as shown on this page. This sample offers a quick and accessible overview of the data's contents and organization.

Our full datasets are available in various formats, depending on the language and type of data you require. These may include XML, JSON, TXT, XLSX, CSV, WAV, MP3, and other file types. Please contact us (Growth.OL@oup.com) if you would like to receive the original sample with full details.

For the high-quality training data required in unsupervised learning and supervised learning, Nexdata provides flexible and customized Large Language Model(LLM) Data Data annotation services for tasks such as supervised fine-tuning (SFT) , and reinforcement learning from human feedback (RLHF).