Ensembling Classifiers for Detecting User Intentions behind Web Queries


Discovering user intentions behind.Net search queries is vital to improving user expertise. Sometimes, this task is seen as a classification drawback, in which a sample of annotated user question intentions are provided to a supervised machine learning algorithm or classifier that learns from these examples and then will classify unseen user queries. This text proposes a new approach based mostly on an ensemble of classifiers. The strategy combines syntactic and semantic features thus on 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.

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