Query Understanding, Divided into Three Parts

  • Holistic understanding of the query to ascertain its topic / category or establish the searcher’s high-level intent.
  • Reductionist understanding to segment the query into components and determine what those components mean.
  • Resolution to transform the results of holistic and reductionist understanding into the query that is executed against the search engine.
  • Language identification. Our example query is in Latin (Classical Latin to be precise). For search applications that support multiple languages, language identification is a critical to enable further query processing.
  • Query categorization, which generally maps the query into a category taxonomy. Our example query could be mapped to History or History of the Roman Empire, depending on the granularity of the categorization.
  • Establishing the searcher’s high-level intent, which presumes a categorization of such intents. For example, if we know that searchers often look up quotations to find the original source, we might recognize the example query as an example of this intent. Other intents might include finding a biography of a person, a textbook about a subject, etc.

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