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Text Annotation
Text Annotation for Language Models
Builds richly labeled language datasets that help models understand meaning, intent, context, and safety constraints.
Core Capabilities
- Named entity extraction and relation mapping
- Intent and sentiment tagging for complex text signals
- Chain-of-thought and reasoning guidance annotations
- Multi-label classification and hierarchical tagging
- OCR validation and structured text extraction
What Clients Get
Model-ready language datasets that reduce ambiguity and improve NLP model accuracy. Fine-tuning corpora aligned to domain semantics and safety policies.
Why It Matters
Text annotation shapes how models interpret nuance, disambiguate intent, and reason over language, especially for generative or decision-support applications.
