Entity extraction identifies and captures specific pieces of information from spoken language, such as names, dates, phone numbers, addresses, and domain-specific terms. Extracted entities become structured data the AI can use.
How does entity extraction work?
When a caller provides information, the system recognizes patterns and context to identify entities. “Next Tuesday at 3pm” yields a date entity and time entity. “John Smith at 555-1234” yields name and phone entities. Modern systems use language models that understand context to extract entities even from complex or informal speech.
Why does entity extraction matter?
Entities are the actionable data within conversations. Booking an appointment requires date, time, and service type entities. Processing a payment requires amount and payment method entities. Accurate extraction enables the AI to take concrete actions rather than just understanding general intent.
Entity extraction in practice
A caller says: “I need a plumber at my rental property on Oak Street sometime next week, maybe Wednesday or Thursday morning.” The system extracts: service type (plumber), location type (rental property), street (Oak Street), date range (next Wednesday-Thursday), and time preference (morning). These entities populate the scheduling request.