In this work we explore the use of decision trees to identify the intent of a user query, based on informational, navigational, and transactional categorization. They are based on decision trees, using the C4.5 implementation. The classifier will be built from a query data set larger than any previously used, allowing the conclusions to have a greater reach. Unlike previous works, we will explore features that have not been evaluated before (e.g. PageRank) combined with features based on text and/or click-through data. The results obtained are very precise and the decision tree obtained allows us to illustrate relations among the variables used for classification determining which of these variables arc more useful in the classification process.
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