Named Entity Recognition Data
Buy and sell named entity recognition data data. Text annotated with person, org, location, and custom entity labels — the NER training data.
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Find Me This Data →Overview
What Is Named Entity Recognition Data?
Named Entity Recognition (NER) data consists of text annotated with labels for people, organizations, locations, dates, monetary values, and custom entity classes. This training data enables machine learning models to automatically identify and classify specific information within unstructured text—solving the challenge of manually extracting critical data from emails, contracts, medical records, legal documents, and customer communications. NER is a subtask of Natural Language Processing (NLP) that transforms how computers understand language and extract meaning from vast volumes of text. The AI training dataset market, which includes NER data, has grown exponentially and is projected to expand from $3.19 billion in 2025 to $3.87 billion in 2026, driven by rising adoption of AI/ML algorithms and demand for high-quality labeled datasets.
Market Data
$3.19B → $3.87B
AI Training Dataset Market Growth (2025–2026)
Source: Research and Markets
21.5%
Projected CAGR
Source: Research and Markets
6 years
Historical Data Coverage (Sample)
Source: Datarade
€5,000 (~$5,400 USD)
TAUS Language Translation Data Pricing (Min)
Source: Datarade
Who Uses This Data
What AI models do with it.do with it.
Data Extraction & Automation
Enterprises use NER to automate extraction of critical information from unstructured documents—emails, invoices, contracts, medical records, and regulatory filings—converting manual processes into structured, machine-readable output.
Customer Support & Routing
NER sorts customer inquiries, feedback, and support tickets by entity type, routing them to appropriate departments for faster resolution and improved response times.
Search, Compliance & Privacy
Publishers and e-commerce platforms use NER to search millions of documents, monitor reviews, and enforce GDPR 'right to be forgotten' requests by identifying and locating personal data at scale.
Data Science & Intelligence
Data scientists leverage NER-annotated datasets to build models that extract actionable insights from large text corpora and support enterprise-scale deep learning applications.
What Can You Earn?
What it's worth.worth.
Research Report (Publication)
€4,034 / $4,490 USD / £3,518 GBP
Report pricing; individual dataset licensing varies by scope and delivery.
Custom Language Translation Data
€5,000+
Entry-level pricing; enterprise and high-volume datasets start at €100,000+.
NER Data Licensing
Varies
Pricing depends on annotation scope, volume, entity types, and delivery frequency.
What Buyers Expect
What makes it valuable.valuable.
Accurate Entity Labeling
Text must be precisely annotated with person, organization, location, date, monetary, and custom entity classes, ensuring models trained on the data perform reliably in production.
Clean & High-Quality Data
Datasets should be free of noise, duplicates, and inconsistencies. Providers are expected to maintain data quality standards and offer verified, production-ready corpora.
Appropriate License & Coverage
Data should include clear licensing (CC-BY, CC-BY-SA, CC-BY-NC-SA, or commercial agreements) and sufficient historical coverage and geographic/language scope for intended applications.
Scalability & On-Demand Production
Buyers expect datasets available in scalable formats, delivered on specified schedules (daily, weekly, etc.), with support for custom annotation and integration capabilities.
Companies Active Here
Who's buying.buying.
Natural Language Processing (NLP) data for AI/ML model development; includes NLP-focused companies purchasing annotated text for NER model training.
Provides parallel translation data and NER-enriched language datasets for e-commerce and colloquial language machine learning; serves global markets across USA, UK, India, Germany, France.
Extract value from unstructured text corpora; build, train, and deploy enterprise-scale NER models for compliance, document processing, and intelligent system automation.
Search document libraries, route customer inquiries, monitor online reviews, and enforce privacy regulations (GDPR) by identifying and extracting named entities at scale.
FAQ
Common questions.questions.
What exactly is Named Entity Recognition data?
NER data is text that has been manually or automatically annotated with labels identifying entities such as people's names, organizations, locations, dates, monetary values, and custom categories. This labeled data trains machine learning models to automatically recognize and classify similar entities in new text.
Why is NER data valuable for businesses?
NER automates the extraction of critical information from massive volumes of unstructured text—emails, contracts, medical records, support tickets, and legal documents. Instead of manual reading and parsing, NER models instantly identify and organize key data, saving time, reducing errors, and enabling faster decision-making and compliance.
How fast is the NER data market growing?
The broader AI training dataset market (which includes NER data) is experiencing explosive growth, expanding from $3.19 billion in 2025 to $3.87 billion in 2026 at a 21.5% compound annual growth rate (CAGR). This growth is driven by rising AI/ML adoption and demand for high-quality labeled datasets.
What pricing should I expect for NER datasets?
Pricing varies significantly based on annotation scope, entity types, dataset volume, and delivery terms. Entry-level custom language data may start at €5,000, while large-scale enterprise datasets can exceed €100,000. Contact providers directly for quotes on specific NER annotation requirements.
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