Named Entity Recognition (NER)
Label people, organizations, locations, domain entities, and arbitrary spans for named entity recognition and information extraction.
Words and phrasesNamed entitiesArbitrary spans
Text Annotation Platform
Text Annotation Platform for NER & NLP
Create NLP training data with named entity recognition (NER), entity annotation, relation extraction, text classification, intent, and sentiment labeling, plus governed review workflows.
No credit card required
Text annotation features
Everything You Need for Complex Text Annotation
Label precise spans, connect entities, preserve document context, structure advanced ontologies, and accelerate annotation with AI.
Relationships & Entity Linking
Connect entities and spans to capture relationships, references, and structured associations.
Context-Aware Annotation
Annotate text while preserving sentence, paragraph, and document-level context for consistent labeling.
Advanced Ontologies
Define hierarchical entity types, nested attributes, relationships, and structured labeling schemas.
AI-Assisted Auto-Labeling
Pre-label entities, spans, and recurring patterns with AI, then review and refine the results.
Nested & Overlapping Entities
Annotate overlapping and nested spans while preserving each entity and its relationships.
Nested spansOverlapping spans
Why AI Teams Choose Unitlab for Text Annotation
Annotate complex text datasets faster with AI-assisted automation, scalable workflows, and lower operational costs.
15X
Faster Text Annotation
90%
Automated Text Annotation
10X
Lower Annotation Costs
Supported text annotation types
All text annotation types in one platform.
Label exact spans, connect relations, classify complete items, and capture structured attributes and properties.
01
Named Entity / Span
Label exact words or passages with reusable entity classes.
02
Relation
Connect entities with typed, directional relationships.
03
Classification
Assign intent, sentiment, topic, or document type.
04
Entity Attributes
Attach typed properties to each labeled entity.
05
Nested Properties
Organize dependent attributes with reusable validation rules.
06
Item Properties
Describe the complete text item with shared properties.
07
Source Text Editing
Correct source content while keeping annotations aligned.
08
Text Window Context
Move through long text with stable offsets and full-source context.
09
Review States
Route text tasks through annotation, review, rework, and approval.
Text dataset curation & annotation workflows
Curate text data and automate annotation workflows.
Search, version, and inspect text datasets, connect AI models, and move annotations through review and approval.
Dataset Versions
Create and manage dataset versions as text data evolves. Track changes, assign work, and keep every version auditable and production-ready.
Semantic Search
Explore large text datasets through semantic understanding instead of manual filters. Find matching documents and passages across varied language.
Embedding View
Visualize dataset structure, identify outliers and labeling issues, and improve text-data quality before training.
Integrated Workflows
Build workflows that connect models, annotation, review, and quality assurance in one continuous loop. Reduce handoffs and keep datasets moving from labeling to approval.
Bring Your Text Model
Connect your own AI models for pre-labeling and model-assisted annotation. Improve accuracy and iterate faster on real-world text datasets.
Solutions
Text annotation solutions for production NLP and LLM data
Build structured language datasets for entity extraction, relations, classification, and large-scale NLP model development.
Named Entity Recognition
Annotate people, organizations, products, locations, dates, and domain-specific entities for NER and information extraction.
Explore NLP use cases
Relation Extraction
Label semantic relationships between entities to train models for structured knowledge extraction and reasoning.
Explore NLP use cases
Intent & Text Classification
Classify messages, documents, and text segments by intent, topic, sentiment, category, or business-specific labels.
Explore ecommerce solutions
LLM & NLP Dataset Preparation
Create structured language datasets with spans, attributes, relations, classifications, and reviewer-validated annotations for NLP and language models.
Explore NLP use casesQuestions, answered
Text Annotation Platform FAQs
Answers about named entity recognition, entity annotation, relation extraction, text classification, intent and sentiment labeling, ontologies, quality workflows, and NLP dataset operations in Unitlab.
Talk with the Unitlab teamWhat text annotation types does Unitlab support?+
Unitlab supports named entity recognition (NER), entity and span labeling, typed relations, text classification, entity attributes, nested properties, Item Properties, configurable text windows, and controlled source text editing.
Text annotation documentationHow does Unitlab support entity and span labeling?+
Annotators select exact words or passages, assign reusable entity classes, add structured attributes, and connect entities with typed relations. Reviewers can inspect and correct every label before approval.
Entity and relation annotation documentationCan Unitlab handle long documents?+
Yes. Configurable text windows let annotators move through long documents while preserving full-source context, stable annotation offsets, entity properties, and relations.
Long-document text annotation documentationHow do ontologies and nested properties work in text annotation?+
Nested ontologies define entity classes, classifications, attributes, relations, and Item Properties. Reusable rules keep structured labels consistent across projects, annotators, and dataset versions.
Properties and relations documentationHow does Unitlab manage text annotation quality and review?+
Configurable workflows route text tasks through annotation, review, rework, and approval. Instructions, assignments, comments, issues, annotation history, dataset versions, and releases keep quality decisions traceable for individual experts and enterprise teams.
Annotation and review documentationCan teams use their own AI models for text pre-labeling?
Yes. Unitlab can bring custom models into the annotation workflow to generate pre-labels and predictions. Annotators review and correct model output instead of starting from zero, while human approval remains part of the governed quality process.
Model integration documentationCan Unitlab curate, annotate, and version text datasets in one platform?
Yes. Teams can curate text data with metadata filters, semantic search, embeddings, similarity, and outlier discovery, then annotate selected samples, review results, and publish controlled dataset versions for reproducible AI development.
Dataset management documentationTEXT ANNOTATION PLATFORM
Build Production-Ready Text Datasets with Unitlab
Annotate, classify, review, and manage complex text data in one AI-assisted workspace. Move faster from raw language data to production-ready NLP datasets with AI assistance and built-in quality control.
