Document -> DocumentParser -> Indexer -> index Query -> QueryParser-> Searcher -> IntentDetector -> QueryRewriter

Query Intent Detection

  • benefits
    • display semantically enriched search results.
    • improve ranking results by triggering a vertical search engine in a certain domain
  • challenging task
    • queries are usually short
    • requires more context beyond the keywords
    • number of intent categories could be very high
  • approches
    • rule-based (precise while coverage is low, bad for scaling)
      • defining patterns for each intent class
      • defining discriminative features for queries to run statistical models
    • statistical methods
      • supervised/unsupervised


extract query vector representations as the feature for the query classification.

In this model, queries are represented as vectors so that semantically similar queries can be captured by embedding them into a vector space.

word vector representations(such as word2vec)

  • supervised method
    • feature engineering (require domain knowledge)
    • lead to state-of-the-art systems
    • use various type of features
      • search sessions
      • click-through data
      • Wikipedia concepts
  • CNN method
    • DO NOT engineering query features
    • use CNN to automatically extract query vectors as the feature
    • architecture
      1. traning the model parameters in the offline time
        • utlize the labeled queries to learn the parameters of CNN and the intent classifier
      2. running the model over new queries in the online time
# train
[Queries with intents] -> (CNN) -> [Query vectors with intents] -> [Classifier]
# predict
[New query] -> (CNN) -> [Query vector] -> (Classifier) -> [Predicted intent]

Search Session