Buy Natural Language Processing (NLP) Data

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Automaton AI
Based in India
Automaton AI
We are a full-stack AI company with a mission to democratize Data. Automaton AI is an AI industry expert who is Transforming how businesses see the world wi... - ShAIp profile banner
Based in USA
Shaip offers a human-in-the-loop data platform and services to support all aspects of managing training data for AI/ML development. From data collection, lic...
Built for AI-Training Data
Quality at Scale
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Knuckle Head
Based in India
Knuckle Head
An end-to-end services on AI Lifecycle for modeling unstructured image, video, and text data. Learn more at - Kieli profile banner
Based in United Kingdom
Kieli is a professional data analytic company dedicated to solves human language challenges using natural language processing techniques.
EPIC Translations
Based in USA
EPIC Translations
We have over 1 million human resources located throughout the world ready for your projects. - Cyabra profile banner
Based in Israel
Cyabra measures the effect of online conversations to uncover authenticity and measure impact. Cyabra's analytic capabilities empower brands, financial servi...

The Ultimate Guide to Natural Language Processing (NLP) Data 2021

Learn about natural language processing (nlp) data analytics, sources, and collection.

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 Automaton AI, ShAIp, Knuckle Head, Kieli, and EPIC Translations.

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 Fully labelled Datasets of Arabic Language for Machine Learning - Text & Audio NLP Data - Kieli by Kieli, TAUS Language Translation Data | Parallel translation for E- Commerce, various language pairs by TAUS, and Agents Republic | Multilingual Conversational AI Training Data via Audio/Voice (50+ languages) by Agents Republic.

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 similar data types to Natural Language Processing (NLP) Data ?

Natural Language Processing (NLP) Data is similar to Annotated Imagery Data. These data categories are commonly used for Machine Learning (ML) and Deep Learning.

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), Deep Learning, and Data Science.