Ensembling Classifiers for Detecting User Intentions behind Web Queries

Alejandro Figueroa, John Atkinson

Resultado de la investigación: Article

8 Citas (Scopus)

Resumen

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.

Idioma originalEnglish
Número de artículo7006341
Páginas (desde-hasta)8-16
Número de páginas9
PublicaciónIEEE Internet Computing
Volumen20
N.º2
DOI
EstadoPublished - 1 mar 2016

Huella dactilar

Classifiers
Syntactics
Learning algorithms
Learning systems
Semantics
Experiments

ASJC Scopus subject areas

  • Computer Networks and Communications

Citar esto

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Ensembling Classifiers for Detecting User Intentions behind Web Queries. / Figueroa, Alejandro; Atkinson, John.

En: IEEE Internet Computing, Vol. 20, N.º 2, 7006341, 01.03.2016, p. 8-16.

Resultado de la investigación: Article

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