Linguistic intelligence for language models
NLP Annotation
Sentiment analysis, entity recognition, relation extraction, and linguistic data labeling for NLP models in 30+ languages.
Overview
What is NLP Annotation?
NLP models require rich, structured linguistic annotations to understand meaning, context, and relationships in text. Our NLP teams combine computational linguistics expertise with domain knowledge.
Why it matters: Raw text contains vast amounts of implicit knowledge. NLP annotation makes this knowledge explicit — turning unstructured language into structured training signals that models can learn from.
Named Entity Recognition
Person, org, location, date, and custom entity types
Sentiment & Intent
Fine-grained annotation with aspect-level labels
Relation Extraction
Entity relationships and dependency parsing labels
30+ Languages
Cross-lingual NLP annotation with native-speaker linguists
Workflow
How We Do It
01
Schema Design
Define annotation schema — entity types, relation categories, sentiment dimensions.
02
Annotator Training
Train annotators on guidelines with calibration examples and IAA testing.
03
Annotation
Expert linguists annotate named entities, relations, sentiment, intent, and discourse structure.
04
IAA Scoring
Inter-annotator agreement calculated with Cohen's Kappa to validate consistency.
05
Export
Deliver in CoNLL, BIO, JSON, or spaCy format with metadata for direct ML use.
Case Study
Legal AI Platform
Legal AI Platform
Annotate legal documents for entity and clause extraction
Solution
Domain-trained legal annotators with custom taxonomy for 15 document types
Results
100K
Documents labeled
96%
Entity precision
8 wks
Duration
Ready to Get Started with NLP Annotation?
Tell us about your project and we'll scope a pilot within 48 hours.