Abstract
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.
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Peetz, MH., Meij, E., de Rijke, M., Weerkamp, W. (2012). Adaptive Temporal Query Modeling. In: Baeza-Yates, R., et al. Advances in Information Retrieval. ECIR 2012. Lecture Notes in Computer Science, vol 7224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28997-2_40
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DOI: https://doi.org/10.1007/978-3-642-28997-2_40
Publisher Name: Springer, Berlin, Heidelberg
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