Ensembling Classifiers for Detecting User Intentions behind Web Queries

Alejandro Figueroa, John Atkinson

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Discovering user intentions behind Web search queries is key to improving user experience. Usually, this task is seen as a classification problem, in which a sample of annotated user query intentions are provided to a supervised machine learning algorithm or classifier that learns from these examples and then can classify unseen user queries. This article proposes a new approach based on an ensemble of classifiers. The method combines syntactic and semantic features so as to effectively detect user intentions. Different setting experiments show the promise of this linguistically motivated ensembling approach, by reducing the ranking variance of single classifiers across user intentions.

Original languageEnglish
Article number7006341
Pages (from-to)8-16
Number of pages9
JournalIEEE Internet Computing
Issue number2
StatePublished - 1 Mar 2016
Externally publishedYes


  • Internet/Web technologies
  • Web search
  • ensemble learning
  • machine learning
  • natural language processing


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