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.