Document structure, intent and taxonomy labeling.
Overview
What is text annotation?
Text annotation labels written content with entity spans, intent categories, document structure and classification tags, so NLP models, chatbots and search can extract meaning at scale.
Search, chatbots and document AI all depend on structured labels over raw text. Precise, consistent annotation is what makes a language model genuinely useful.
Section, heading and table structure labeling for document AI.
Multi-class and hierarchical intent labels for conversational AI.
Custom category and topic taxonomies at any depth.
0.85+ Cohen's Kappa target on every categorical annotation project.
Workflow
How we do it
- 01Corpus analysis
We analyze your corpus to define entity types, label taxonomy and annotation structure.
- 02Guideline development
Detailed guidelines with examples and edge-case decisions, built with your team.
- 03Expert annotation
Linguist-trained annotators label entities, intent, structure and classification tags.
- 04IAA calibration
Inter-annotator agreement measured, with calibration sessions to align the team.
- 05Delivery
Final datasets in JSON, CSV or your custom format, with IAA scores.
Case study
Classify 2M support documents into a 200-node taxonomy
Hierarchical annotation pipeline with domain-specialist reviewers and IAA monitoring.
Ready to get startedwith text annotation?
Tell us about your project and we will scope a pilot within 48 hours.