Structured signal from unstructured text
Text Annotation
Document structure, intent classification, and taxonomy labeling for search, chatbots, and document AI.
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 engines can extract meaning at scale.
Why it matters: Search engines, chatbots, and document AI all depend on structured labels layered over raw text. Precise, consistent annotation is what turns a language model from plausible to genuinely useful.
Document Structure
Section, heading, and table structure labeling for document AI
Intent Classification
Multi-class and hierarchical intent labels for conversational AI
Taxonomy Labeling
Custom category and topic taxonomies at any depth
High Agreement
0.85+ Cohen's Kappa target on every categorical annotation project
Workflow
How We Do It
01
Corpus Analysis
We analyze your text corpus to define entity types, label taxonomy, and annotation schema.
02
Guideline Development
Detailed annotation guidelines with examples and edge-case decisions built collaboratively.
03
Expert Annotation
Linguist-trained annotators label entities, intent, structure, and classification tags.
04
IAA Calibration
Inter-annotator agreement measured and calibration sessions align team-wide consistency.
05
Delivery
Final datasets delivered in JSON, CSV, or your custom format with IAA scores.
Case Study
Enterprise Search Vendor
Enterprise Search Vendor
Classify 2M support documents into a 200-node taxonomy
Solution
Hierarchical annotation pipeline with domain-specialist reviewers and IAA monitoring
Results
2M
Documents classified
94%
Classification accuracy
10 wks
Delivery
Ready to Get Started with Text Annotation?
Tell us about your project and we'll scope a pilot within 48 hours.