This Natural Language Processing (NLP) dataset contains a part of the MySQL Corpus Nummorum (CN) database. It covers Greek and Roman coins from ancient Thrace, Moesia Inferior, Troad and Mysia.

The dataset contains 7,900 coin descriptions (or designs) created by the members of the CN project. Most of them are actual coin designs which can be linked through our relational database to the matching CN coins, types and their images. However, some of them (about 450) were only created for the training of the NLP model.

There are nine different MySQL tables:

1. data_coins: contains the data of all coins in the CN database
2. data_coins_images: contains data of all images in the CN database
3. data_coins_imagesets: contains the image pairs for the CN coins
4. data_designs: contains every coin description in German, English and Bulgarian
5. data_types: contains the data of alle coin types the Cn database
6. nlp_hierarchy: contains the classes and subclasses of all entity categories
7. nlp_list_entities: contains the data of all nlp entities in the CN database
8. nlp_relation_extraction_en_v2: contains the annotations for the training of our NLP model
9. nlp_training_designs: contains the coin designs used for training our NLP model

Only tables 8 and 9 are important for NLP training, as they contain the descriptions and the corresponding annotations. The other tables (data_...) make it possible to link the coin descriptions with the various coins and types in the CN database. It is therefore also possible to provide the CN image data sets with the appropriate descriptions (CN - Coin Image Dataset and CN - Object Detection Coin Dataset). The other NLP tables provide information about the entities and relations in the descriptions and are used to create the RDF data for the nomisma.org portal. The tables of the relational CN database can be related via the various ID columns using foreign keys.

For easier access without MySQL, we have attached two csv files with the descriptions in English and German and the annotations for the English designs. The annotations can be related to the descriptions via the Design_ID column.

During the summer semester 2024, we held the "Data Challenge" event at our Department of Computer Science at the Goethe-University. Our students could choose between the Object Detection dataset and a Natural Language Processing dataset as their challenge. We gave the teams that decided to take part in the NLP challenge this dataset with the task of trying out their own ideas. Here are the results:

• LLM_RE Pipeline
• Coin description embeddings
• NLP coin app

Now we would like to invite you to try out your own ideas and models on our coin data.

If you have any questions or suggestions, please, feel free to contact us.

1. Specifications Format : Audio format: 48kHz, 16bit, uncompressed wav, mono channel; Vedio format: MP4

Recording Environment : In-car;1 quiet scene, 1 low noise scene, 3 medium noise scenes and 2 high noise scenes

Recording Content : It covers 5 fields: navigation field, multimedia field, telephone field, car control field and question and answer field; 500 sentences per people

Speaker : Speakers are evenly distributed across all age groups, covering children, teenagers, middle-aged, elderly, etc.

Device : High fidelity microphone; Binocular camera

Language : 20 languages

Transcription content : text

Accuracy rate : 98%

Application scenarios : speech recognition, Human-computer interaction; Natural language processing and text analysis; Visual content understanding, etc.

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go Natural Language Processing (NLP) Data support instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/speechrecog?source=Datarade

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

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 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).

Dataset

This dataset was created by shanghai panda

Released under Apache 2.0

Contents

This data asset contains data files of text extracted from pdf reports on the Development Experience Clearinghouse (DEC) for the years 2011 to 2021 (as of July 2021). It includes three specific "Document types" identified by the DEC: Final Contractor/Grantee Report, Final Evaluation Report, and Special Evaluation. Each PDF document labeled as one of these three document types and labeled with a publication year from 2011 to 2021 was downloaded from the DEC in July 2011. The dataset includes text data files from 2,579 Final Contractor/Grantee Reports, 1,299 Final Evaluation reports, and 1,323 Special Evaluation reports. Raw text from each of these PDFs was extracted and saved as individual csv files, the names of which correspond to the Document ID of the PDF document on the DEC. Within each csv file, the raw text is split into paragraphs and corresponding sentences. In addition, to enable Natural Language Processing of the data, the sentences are cleaned by removing unnecessary special characters, punctuation, and numbers, and each word is stemmed to its root to remove inflections (e.g. pluralization and conjugation). This data could be used to analyze trends in USAID's programming approaches and terminology. This data was compiled for USAID/PPL/LER with the Program Cycle Mechanism.

The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.

This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.

The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).

The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).

The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.

The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.

The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.

The data sources used are:

- The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/

- CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID

- MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID

- CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data

- TREC Health Misinformation track https://trec-health-misinfo.github.io/

- TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html

The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).

The entries in the dataset contain the following information:

- Claim. Text of the claim.

- Claim label. The labels are: False, and True.

- Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.

- Original information source. Information about which general information source was used to obtain the claim.

- Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.

Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).

References

- Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596

- Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.

- Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.

- Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

- Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.

- Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.

- Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

- Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.

- Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.

- Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.

- Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.

- Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

Healthcare NLP Solution Market Outlook

The global Healthcare NLP Solution market size was valued at approximately USD 1.8 billion in 2023 and is projected to reach around USD 7.5 billion by 2032, exhibiting a CAGR of 17.1% during the forecast period. This impressive growth trajectory is primarily driven by the increasing adoption of advanced technologies in healthcare, such as natural language processing (NLP), aimed at improving patient care and operational efficiency.

One significant growth factor for the Healthcare NLP Solution market is the rising volume of unstructured clinical data. Healthcare organizations generate massive amounts of data, including clinical notes, patient records, and research papers. Traditional data processing methods are often inadequate to handle this unstructured data efficiently. NLP solutions can process, analyze, and interpret this data to extract meaningful insights, thus supporting clinical decision-making and improving patient outcomes. Consequently, the demand for NLP solutions in healthcare is surging.

Another crucial growth driver for the market is the increasing focus on precision medicine and personalized healthcare. NLP solutions enable healthcare providers to analyze large datasets to identify patterns and trends that can help in personalized treatment plans. By leveraging NLP technologies, clinicians can tailor treatments to individual patient profiles, thus enhancing the effectiveness of medical interventions. This personalized approach not only improves patient care but also contributes to the rapid growth of the Healthcare NLP Solution market.

Moreover, the integration of NLP solutions with electronic health records (EHRs) is significantly boosting market growth. EHRs have become ubiquitous in healthcare settings, and the addition of NLP capabilities enhances their utility by enabling more effective data retrieval and analysis. This integration facilitates better patient management, reduces the likelihood of errors, and improves clinical workflows. As healthcare providers continue to adopt EHR systems, the demand for integrated NLP solutions is anticipated to grow, further propelling market expansion.

Natural Language Processing (NLP) Software is at the forefront of transforming the healthcare industry by enabling the efficient processing of unstructured data. This software leverages advanced algorithms to understand and interpret human language, making it possible to extract valuable insights from clinical notes, patient feedback, and research articles. By automating these processes, NLP software reduces the time and effort required for data analysis, allowing healthcare professionals to focus more on patient care. The integration of NLP software into healthcare systems is not only enhancing operational efficiency but also paving the way for more personalized and precise medical treatments. As the demand for data-driven decision-making grows, the role of NLP software in healthcare is becoming increasingly indispensable.

From a regional perspective, North America currently holds the largest market share in the Healthcare NLP Solution market, driven by the early adoption of advanced healthcare technologies and substantial investments in healthcare infrastructure. However, the Asia Pacific region is expected to exhibit the highest CAGR during the forecast period. Factors such as increasing healthcare expenditures, growing awareness of advanced healthcare technologies, and supportive government initiatives are driving market growth in this region. Europe and Latin America are also showing significant growth potential, driven by improving healthcare systems and increasing adoption of digital health solutions.

Component Analysis

The component segment of the Healthcare NLP Solution market is bifurcated into software and services. The software segment includes NLP tools and platforms designed to analyze unstructured clinical data, while the services segment encompasses implementation, training, and maintenance services required to deploy these solutions effectively. The software segment is currently dominating the market, driven by the increasing need for advanced analytics tools to manage and interpret vast amounts of healthcare data.

NLP software solutions are gaining traction due to their ability to streamline clinical documentation processes. These tools can automatically transcribe and structure clinical notes, significantly reducing

Being an Image labeling expert, we have immense experience in various types of data annotation services. We Annotate data quickly and effectively with our patented Automated Data Labelling tool along with our in-house, full-time, and highly trained annotators.

We can label the data with the following features:

1. Image classification
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4. Image tagging
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8. Custom user-defined labeling

Data Services we provide:

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2. Data cleaning
3. Data mining
4. Data labeling
5. Data management​

We have an AI-enabled training data platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place.

Natural Language Processing (NLP) Software Market Outlook

The global market size for Natural Language Processing (NLP) software is projected to grow from USD 13.5 billion in 2023 to USD 50.1 billion by 2032, at a compound annual growth rate (CAGR) of 15.7%. The significant growth in this market is primarily driven by the increasing demand for advanced text analytics, the proliferation of big data, and the rising adoption of AI-based applications across various industries.

One of the primary growth factors in the NLP software market is the surge in demand for enhanced customer experiences across sectors such as retail, banking, and healthcare. Companies are increasingly leveraging NLP technologies to understand customer sentiments, preferences, and behaviors by analyzing unstructured text data from various sources, including social media, emails, and customer reviews. This capability to derive actionable insights from vast amounts of textual data is propelling the adoption of NLP solutions.

Another considerable growth factor is the rapid advancement in machine learning algorithms and computational linguistics, which has significantly improved the accuracy and efficiency of NLP applications. The integration of neural networks and deep learning techniques has enabled more sophisticated language models, such as BERT and GPT-3, which can perform a wide array of tasks from language translation to complex question-answering. These technological advancements are widening the application scope of NLP software, further fueling market growth.

The inclusion of NLP in various business processes is also driven by the need for automation and operational efficiency. By automating routine tasks such as document summarization, email filtering, and real-time translation, organizations can significantly reduce operational costs and improve productivity. The COVID-19 pandemic has further accelerated the demand for such automated solutions, as remote working environments necessitate more efficient communication and collaboration tools, which NLP software can provide.

Geographically, North America holds the largest market share for NLP software, attributed to the presence of major technology giants, a robust IT infrastructure, and early adoption of advanced technologies. However, the Asia-Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid digitalization, increasing investments in AI technologies, and a growing number of startups focused on NLP innovations.

Component Analysis

The NLP software market is segmented by components into software and services. The software segment includes standalone NLP solutions and platform-based offerings that provide extensive functionalities for text analysis, sentiment analysis, and machine translation, among others. Standalone NLP software solutions are witnessing significant adoption due to their ability to handle specialized tasks and offer tailored functionalities that meet specific business needs. These software solutions are continuously evolving with advanced features and improved accuracy owing to ongoing research and development in the field of AI and machine learning.

The platform-based offerings are gaining traction as they provide comprehensive suites that integrate various NLP functionalities into a single platform. These platforms allow businesses to deploy a range of NLP applications without the need for multiple distinct software solutions. This integration simplifies the implementation process and offers a more streamlined user experience. Major tech companies are investing heavily in developing such platforms to offer end-to-end NLP solutions, further driving the market growth for this segment.

On the services front, the segment comprises professional services and managed services. Professional services include consulting, system integration, and support and maintenance services. Organizations often require expert consulting to identify the best NLP applications suited to their needs, as well as integration services to ensure seamless deployment within their existing systems. The demand for such professional services is increasing as more businesses recognize the potential of NLP technologies to transform their operations.

Managed services involve outsourcing the management of NLP applications to third-party service providers. This model is becoming increasingly popular, especially among small and medium enterprises (SMEs) that may lack the in-house expertise or resources to manage complex NLP soluti

Snapshot img

Natural Language Processing Market Size 2024-2028

The natural language processing market size is forecast to increase by USD 125.82 billion at a CAGR of 42.93% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for NLP applications in various industries. The advancements in AI and machine learning technology are enabling machines to understand and interpret human language more accurately, making NLP an essential tool for businesses seeking to improve customer engagement and automate processes. However, the ambiguity and complexities of natural human language pose challenges for NLP systems, requiring continuous research and development to enhance their capabilities. Key trends in the market include the integration of NLP with machine learning algorithms, the use of deep learning techniques, and the adoption of cloud-based NLP solutions. These advancements are expected to drive market growth and provide opportunities for companies to offer innovative solutions to meet the evolving needs of businesses.

What will be the Size of the Natural Language Processing Market During the Forecast Period?

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The market is experiencing significant growth due to the increasing adoption of advanced technology, including speech recognition, sentiment analysis, virtual agents, and chatbots. These AI-powered solutions enable more effective text analytics, interactive voice response, and optical character recognition, driving digital transformation across various industries. The market is expected to continue expanding, fueled by the proliferation of AI technologies such as conversational AI, data management, and machine learning. Cloud-based solutions and 5G infrastructure are key enablers, facilitating the deployment of AI-powered chatbots, digital assistants, and data security measures. Consumer demand for personalized and efficient communication is further accelerating market growth.
However, data security concerns remain a significant challenge, necessitating strong data management and encryption solutions. Overall, the market is poised for continued expansion, with applications spanning customer experience, smart devices, social media platforms, and communication infrastructure.

How is this Natural Language Processing Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US


  APAC

    China
    India


  Europe

    Germany
    UK


  South America



  Middle East and Africa

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period. The market experienced significant growth in 2023, driven by the increasing demand for automation, data-driven decision-making, and the need to extract insights from unstructured data. Advanced technologies, such as speech recognition, sentiment analysis, virtual agents, and chatbots, are integral components of NLP solutions. Major market players, including Google (Alphabet), Microsoft, Amazon, and IBM, offer comprehensive NLP suites for various applications, including text mining, virtual assistants, and sentiment analysis. The market's expansion is fueled by the integration of AI technologies, conversational AI, and predictive analytics. Additionally, the adoption of cloud-based solutions, 5G infrastructure, and multi-cloud strategies is transforming the industry.

Get a glance at the market report of share of various segments Request Free Sample

The solution segment was valued at USD 4.9 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 29% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market size of various regions, Request Free Sample

The market is experiencing substantial growth due to the presence of leading technology corporations and research institutions in the region. The US, in particular, holds a significant market share, driven by the dominance of major industrial companies and the widespread adoption of NLP in various industries such as healthcare, banking, and customer services. In demand are NLP solutions offering advanced language comprehension, sentiment analysis, and chatbot capabilities. Factors fueling this market's optimistic outlook include technology advancements, increased AI investments, and the growing need for effective language processing in dat

We have an in-house team of Data Scientists & Data Engineers along with sophisticated data labeling, data pre-processing, and data wrangling tools to speed up the process of data management and ML model development. We have an AI-enabled platform "ADVIT", the most advanced Deep Learning (DL) platform to create, manage high-quality training data and DL models all in one place. ADVIT simplifies the working of your DL Application development.

The Natural Language Processing (NLP) Data of in-car speech covers 20+ languages, including read, wake-up word, commend word, code-swithing, multimodal and noise data.

Natural Language Processing (NLP) in Healthcare and Life Sciences Market Outlook

The global market size for Natural Language Processing (NLP) in Healthcare and Life Sciences was valued at approximately USD 3 billion in 2023 and is projected to reach USD 15 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 20%. This impressive growth is driven by the increasing adoption of digital health technologies and the rising need for data-driven decision-making in healthcare and life sciences. The ability of NLP to transform unstructured data into meaningful, actionable insights is a significant growth factor in this market.

One of the primary growth factors for the NLP in Healthcare and Life Sciences market is the burgeoning volume of healthcare data. With the proliferation of electronic health records (EHRs), medical records, research papers, and patient-generated data, there is an overwhelming need to manage and analyze this data effectively. NLP technologies enable healthcare providers to extract valuable insights from unstructured data, thereby improving clinical decision-making, enhancing patient outcomes, and streamlining operations. The integration of NLP in healthcare systems facilitates efficient data management and fosters evidence-based practices, which is crucial in the era of personalized medicine.

Another significant growth driver is the escalating demand for improved patient care and personalized treatment. NLP technologies help in identifying patterns and trends in patient data, which can be used to tailor individualized treatment plans. For instance, NLP can analyze patient histories, lab results, and notes to recommend specific treatments or flag potential health issues. Additionally, NLP-powered virtual assistants and chatbots are gaining popularity for providing round-the-clock patient support and monitoring, leading to enhanced patient engagement and satisfaction. This growing emphasis on patient-centric care is fueling the adoption of NLP technologies in the healthcare sector.

The increasing investment in healthcare IT infrastructure and the rising collaboration between technology firms and healthcare providers are also propelling market growth. Governments and private organizations are making substantial investments in digital health initiatives, which include the implementation of advanced analytics and NLP technologies. Furthermore, partnerships between tech giants and healthcare institutions are fostering innovation and accelerating the development of NLP solutions tailored to the healthcare domain. These collaborations are not only enhancing the capabilities of NLP technologies but also ensuring their widespread adoption across various healthcare settings.

From a regional perspective, North America holds a prominent position in the NLP in Healthcare and Life Sciences market due to its advanced healthcare infrastructure, high adoption rates of cutting-edge technologies, and significant investment in research and development. Europe follows closely, with a strong focus on digital health transformation and regulatory support for AI integration in healthcare. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid digitization of healthcare systems, increasing government initiatives, and the presence of a large patient population. These regional trends highlight the global momentum towards adopting NLP technologies in the healthcare and life sciences sectors.

Component Analysis

The NLP in Healthcare and Life Sciences market is segmented by component into software, hardware, and services. The software segment is expected to dominate the market due to the extensive use of NLP software tools and platforms in various healthcare applications. These software solutions include text mining, speech recognition, and machine learning algorithms that enable the extraction and analysis of unstructured data. The flexibility and scalability of software solutions make them a preferred choice for healthcare providers aiming to enhance their data analytics capabilities.

The hardware segment, though smaller, plays a critical role in supporting the deployment of NLP technologies. Hardware components such as servers, storage devices, and advanced computing systems are essential for processing large volumes of healthcare data efficiently. With the increasing complexity and volume of data, there is a growing need for robust hardware infrastr

princeton-nlp/prolong-data-64K

[Paper] [HF Collection] [Code] ProLong (Princeton long-context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/prolong-data-64K.

DSE-project-sem-5/Emotion-Data-for-nlp dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for "imdb"

  Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.

Dataset

This dataset was created by Hitesh Kandala

Contents

This dataset is the public medical text record (progress notes) written in Japanese.

Any researchers can use this dataset without privacy issues.

CC BY-NC 4.0

crowd.zip: 9,756 pseudo progress notes written by crowd workers

crowd_evaluated.zip: 83 pseudo progress notes with authentic quality written by crowd workers

MD.zip: 19 pseudo progress notes written by medical doctors

Reference:

Kagawa, R., Baba, Y., & Tsurushima, H. (2021, December). A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 3534-3543). IEEE.

http://hdl.handle.net/2241/0002002333

The supplemental files of the paper are here: https://github.com/rinabouk/HMData2021