Sentiment, entities, relations and linguistic labels.
Overview
What is NLP annotation?
NLP models need rich, structured linguistic annotation to understand meaning, context and relationships in text. Our teams pair computational linguistics with domain knowledge.
Raw text carries vast implicit knowledge. NLP annotation makes it explicit, turning unstructured language into training signals a model can learn from.
Person, org, location, date and custom entity types.
Fine-grained annotation with aspect-level labels.
Entity relationships and dependency parsing labels.
Cross-lingual annotation with native-speaker linguists.
Workflow
How we do it
- 01Schema design
Define the schema: entity types, relation categories and sentiment dimensions.
- 02Annotator training
Train annotators on guidelines with calibration examples and IAA testing.
- 03Annotation
Expert linguists annotate entities, relations, sentiment, intent and discourse structure.
- 04IAA scoring
Inter-annotator agreement calculated with Cohen's Kappa to validate consistency.
- 05Export
Deliver in CoNLL, BIO, JSON or spaCy format with metadata for direct ML use.
Case study
Annotate legal documents for entity and clause extraction
Domain-trained legal annotators with a custom taxonomy for 15 document types.
Ready to get startedwith NLP annotation?
Tell us about your project and we will scope a pilot within 48 hours.