Intent Classification
Definition
Intent classification assigns a user input to a predefined intent class.
The method is frequently used in chatbots, routing, and NLP. It requires a meaningful intent schema and examples; open or ambiguous inputs should have a fallback strategy.
Simply explained
How to initially sort a query into categories like “change appointment,” “invoice,” or “malfunction.”
Why it matters in practice
Relevant for support automation and routing when recurring query types can be clearly defined.
Example from business
A service mailbox classifies queries into complaint, delivery status, invoice, and unknown, then routes them accordingly.
Limits and pitfalls
- Too many overlapping intents; no Unknown/Fallback; training from unchecked historical labels; multiple intents ignored.
Origin & history
Intent classification is an established method in spoken language understanding and conversational AI.
Use in mid-sized companies
- Possible applications: ticket triage, chatbots, voicebots, and routing to appropriate workflows.
Frequently asked questions
Is every input unambiguous?
No. What to do in case of uncertainty? Use threshold, fallback, or human review.
Sources
- NLU Training Data · Rasa, NLU Training Data
- Fallback und Handoff · Rasa 3.x, Fallback und Handoff
Source verification: 11.10.2026.
Editorially reviewed on: