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Encyclopedic queries express the intent of obtaining information typicallyavailable in encyclopedias, such as biographical, geographical or historicalfacts. In this paper, we train a classifier for detecting the encyclopedicintent of web queries. For training such a classifier, we automatically labeltraining data from raw query logs. We use click-through data to select positiveexamples of encyclopedic queries as those queries that mostly lead to Wikipediaarticles. We investigated a large set of features that can be generated todescribe the input query. These features include both term-specific patterns aswell as query projections on knowledge bases items (e.g. Freebase). Resultsshow that using these feature sets it is possible to achieve an F1 score above87%, competing with a Google-based baseline, which uses a much wider set ofsignals to boost the ranking of Wikipedia for potential encyclopedic queries.The results also show that both query projections on Wikipedia article titlesand Freebase entity match represent the most relevant groups of features. Whenthe training set contains frequent positive examples (i.e rare queries areexcluded) results tend to improve.

"Piaf" vs "Adele": classifying encyclopedic queries using automatically labeled training data