Abstract
In this paper we report results for automatic classification of queries in a wide set of facets that are useful to the identification of query intent. Our hypothesis is that the performance of single-faceted classification of queries can be improved by introducing information of multi-faceted training samples into the learning process. We test our hypothesis by performing a multi-faceted classification of queries based on the combination of correlated facets. Our experimental results show that this idea can significantly improve the quality of the classification. Since most of previous works in query intent classification are oriented to the study of single facets, these results are a first step to an integrated query intent classification model.
This research was partially funded by the Coordinated Research Grant TIN2009-15536-C02-1 of the Spanish Ministry of Science and Technology.
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González-Caro, C., Baeza-Yates, R. (2011). A Multi-faceted Approach to Query Intent Classification. In: Grossi, R., Sebastiani, F., Silvestri, F. (eds) String Processing and Information Retrieval. SPIRE 2011. Lecture Notes in Computer Science, vol 7024. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24583-1_36
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