P.Saleiro, L.Sarmento, ”Piaf vs Adele: Classifying encyclopedic queries using automatically labeled training data“, Open Research Areas in Information Retrieval (OAIR 2013), 10th International Conference in the RIAO series, May 2013
Encyclopedic queries express the intent of obtaining information typically available in encyclopedias, such as biographical, geograph- ical or historical facts. In this paper, we train a classifier for detect- ing the encyclopedic intent of web queries. For training such a clas- sifier, we automatically label training data from raw query logs. We use click-through data to select positive examples of encyclopedic queries as those queries that mostly lead to Wikipedia articles. We investigated a large set of features that can be generated to describe the input query. These features include both term-specific patterns as well as query projections on knowledge bases items (e.g. Free- base). Results show that using these feature sets it is possible to achieve an F1 score above 87%, competing with a Google-based baseline, which uses a much wider set of signals to boost the rank- ing of Wikipedia for potential encyclopedic queries. The results also show that both query projections on Wikipedia article titles and Freebase entity match represent the most relevant groups of features. When the training set contains frequent positive examples (i.e rare queries are excluded) results tend to improve.