NLU Entity Extraction — Slot Filling & Domain Entity Recognition

I will build a custom entity extraction system for your NLU application — covering entity type definition, annotated training data creation, model training for your specific entity types (dates, locations, product names, quantities, or domain-specific entities), and integration with your intent classification system for complete NLU understanding. Entity extraction in NLU requires handling the same entity value expressed in multiple surface forms — a date entity must recognise "tomorrow," "next Tuesday," "15th March," and "03/15" as the same type of information, and a product entity must recognise model numbers, product names, and informal references as referring to the same catalogue item.

The entity extractor covers entity type and role schema design, training data annotation, model training with entity-level precision/recall, value normalisation for entities with multiple surface forms (dates, numbers, and synonyms), and integration with your intent classifier.