Buy Natural Language Processing (NLP) Data

Natural language processing (NLP) data gives an overview of how computer systems are programmed to understand, interpret, and manipulate human language. Datarade helps you find NLP data APIs, datasets, and databases. Learn more →
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Kitchen photos / videos
by Automaton AI
Automaton AI
1K videos
1 country covered
Photos of the day-to-day kitchen photos
5M Million words per language
100% words
7 countries covered
When settling an agreement, there should be no doubt about the conditions and mutual obligations. Contracts and agreements are subject to close scrutiny, so ...
240 countries covered
Machine learning / Deep Learning model development services
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InData Labs
Based in Cyprus
InData Labs
InData Labs is a 50 people strong data science firm and AI-powered solutions provider with its own R&D center that provides advanced data analysis to busines...
GWH Ventures
Based in USA
GWH Ventures
GWH Ventures is a data provider offering Stock Market Data, Alternative Credit Data, and Natural Language Processing (NLP) Data . They are headquartered in U... - Accern profile banner
Based in USA
Researchers, business analysts, data science teams, and developers use Accern to build and deploy finance specific AI use cases powered by adaptive NLP. Alli...
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The Ultimate Guide to Natural Language Processing (NLP) Data 2021

Learn everything about Natural Language Processing (NLP) Data . Understand data sources, popular use cases, and data quality.

What is Natural Language Processing (NLP) Data?

Artificial intelligence continues to gain more traction in contemporary technological advancement. As this field spreads into various sectors, it has found applications in machine learning techniques through aspects such as natural language processing where computer systems are being programmed to comprehend, interpret and manipulate human languages. This development has made a lots of well-publicised strides, as seen in Google’s Android Assistant, Apple’s Siri, and Amazon’s Alexa voice assistant programs that understand human language and are used to process data.

How is Natural Language Processing (NLP) Data collected?

NLP data is collected through rule-based models which are considered the oldest means that were hand-written and hand-coded during the earlier stages of NLP development. On the other hand, the more modern statistical-based models calls on machine learning techniques to infer and interpret language learning rules through the analysis of real-world instances of large datasets. Through machine learning algorithms, NLP data is collected from the programs that are designed to identify and learn recurring patterns to focus autonomously on certain areas of the input text.

What are the typical attributes of Natural Language Processing (NLP) Data?

Natural language processing data is made up of machine learning algorithms currently in use, statistical models for computer mapping of information, and rule-based modeling approaches. These attributes combine efforts to help computer systems process human language data. Furthermore, some of the aspects that make up NLP data include text-to-speech or speech-to-text conversions, machine translation from one language to another (e.g. Google Translate), categorizing, indexing, and summarizing written documents, and the ability of computer systems to identify moods and opinions within the text and voice-based data.

What is Natural Language Processing (NLP) Data used for?

NLP data is used by computer systems to help in the breakdown of large categories of human language data into smaller, shorter, concise, and more logical components with the sole purpose of comprehending the semantic and syntactic purpose of spoken and written human language. Advancement in machine learning means that with more NLP data, it is now possible for computer systems to analyze data at a much faster rate helping bridge the gap in a large volume of data that is accumulated due to slow processing. Machines that are designed with machine learning algorithms can analyze and comprehend more language data than humans because they have the ability to process more language patterns, thanks to NLP data.

How can a user assess the quality of Natural Language Processing (NLP) Data?

When determining the quality of NLP data, users should apply the data quality index (DQI) techniques to determine that the data entries are correct, there are no duplicates of specific data units, and that the referential integrity of the data is correct for a natural language processing database. A user can apply the core principles of DQI to filter out any traces of bias in the dataset while also assessing the validity of counterfactual data.

Who are the best Natural Language Processing (NLP) Data providers?

Finding the right Natural Language Processing (NLP) Data provider for you really depends on your unique use case and data requirements, including budget and geographical coverage. Popular Natural Language Processing (NLP) Data providers that you might want to buy Natural Language Processing (NLP) Data from are InData Labs, GWH Ventures, Accern, Brain Company, and Sensefolio.

Where can I buy Natural Language Processing (NLP) Data ?

Data providers and vendors listed on Datarade sell Natural Language Processing (NLP) Data products and samples. Popular Natural Language Processing (NLP) Data products and datasets available on our platform are Kitchen photos / videos by Automaton AI , TAUS: Parallel text, Legal, contracts and obligations - Languages: See below by TAUS, and ML/DL model development services by Automaton AI .

How can I get Natural Language Processing (NLP) Data ?

You can get Natural Language Processing (NLP) Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Natural Language Processing (NLP) Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Natural Language Processing (NLP) Data APIs, feeds and streams to download the most up-to-date intelligence.

What are the most common use cases for Natural Language Processing (NLP) Data ?

The top use cases for Natural Language Processing (NLP) Data are Machine Learning (ML) and Data Science.