We present an approach to query modeling that uses the temporal distribution of documents in an initially retrieved set of documents. Such distributions tend to exhibit bursts, especially in news-related document collections. We hypothesize that documents in those bursts are more likely to be relevant and update the query model with the most distinguishing terms in high-quality documents sampled from bursts. We evaluate the effectiveness of our models on a test collection of blog posts. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Peetz, M. H., Meij, E., De Rijke, M., & Weerkamp, W. (2012). Adaptive temporal query modeling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7224 LNCS, pp. 455–458). https://doi.org/10.1007/978-3-642-28997-2_40
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