In this work, we consider the problem of classifying time-sensitive queries at different temporal granularities (day, month, and year). Our approach involves performing Bayesian analysis on time intervals of interest obtained from pseudo-relevant documents. Based on the Bayesian analysis we derive several effective features which are used to train a supervised machine learning algorithm for classification. We evaluate our method on a large temporal query workload to show that we can determine the temporal class of a query with high precision.
CITATION STYLE
Gupta, D., & Berberich, K. (2015). Temporal query classification at different granularities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9309, pp. 156–164). Springer Verlag. https://doi.org/10.1007/978-3-319-23826-5_16
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