Intent classification
Intent classification is the assignment of an input, such as a user query or utterance, to a category representing its purpose, goal, or information need.
Natural Language Processing in Action, Second Edition
Intent classification assigns text to a category representing the user's purpose or communicative intent.
- How it is used
- Selects chatbot responses and conversation paths while supporting request analytics and semantic routing.
- How deep it goes
- Builds a two-category keyword rule and explains fuzzy classification and vector-based routing, without detailing classifier training.
- Examples
- Uses a Python greeting check, varied light-switching requests, and safeguarding needs in a math tutor chatbot.
- What you can do after
- Build it from scratch as a two-category Python keyword classifier and evaluate/understand when broader intent recognition matters.
Read how Natural Language Processing in Action, Second Edition explains intent classification →
AI-Powered Search
Query classification is the assignment of a search query to one or more predefined intent, topic, or behavior classes.
- How it is used
- Classifies incoming queries against known document categories to support contextual query interpretation.
- How deep it goes
- Works through a configurable SKG traversal and scored examples; defers the assembled end-to-end semantic search layer.
- Examples
- Contrasts category scores for driver, driver AND taxi, and driver AND install in an indexed search collection.
- What you can do after
- Apply it via an SKG traversal to rank query categories and use them to contextualize related terms.
Effective Conversational AI
Intent classification is the assignment of a user utterance to one of a set of predefined intent categories.
- How it is used
- Routes conversational users by correctly identifying their goals and determining when the assistant can respond, act, or escalate.
- How deep it goes
- Explains training-data preparation, classifier testing, performance signals, and maintenance, but does not descend into implementation code.
- Examples
- Uses chat logs, call-center transcripts, emails, open-ended greetings, generic replies, repeated requests, opt-outs, and escalations as examples or signals.
- What you can do after
- Diagnose/debug weak conversational understanding by improving representative training data, testing accuracy, and tracking user-friction signals.
Read how Effective Conversational AI explains intent classification →
The Art of AI Product Development
The categorization of a search query into a type or intent that determines how it is processed or retrieved.
- How it is used
- Routes queries through different retrieval pipelines and prioritizes sources according to recognized intent.
- How deep it goes
- Explains classification through query categories and retrieval parameters, but does not detail its implementation.
- Examples
- Uses AI questions spanning technical advancements, market trends, product integration, healthcare, finance, SaaS, and workflow automation.
- What you can do after
- Operate/configure retrieval pipelines by assigning query types and setting intent-specific source priorities.
Read how The Art of AI Product Development explains intent classification →
Related concepts
- utteranceuses · An utterance is a spoken or written piece of natural-language input produced by a user to express an intended meaning.
- knowledge graphused by · A knowledge graph is a graph-structured representation of entities and their semantically meaningful relationships, often including associated attributes or facts.
- branchused by · Branching is the selection among alternative execution paths according to a condition, outcome, or program state.
- f-scoremeasured by · An F-score combines precision and recall using a weighted harmonic mean; the F1 score weights them equally.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.