Abstract
In this article, we apply a graph-based approach for pseudo-relevance feedback. We model term co-occurrences in a fixed window or at the document level as a graph and apply a random walk algorithm to select expansion terms. Evaluation of the proposed approach on several standard TREC and CLEF collections including the recent TREC-Microblog dataset show that this approach is in line with state-of-the-art pseudo-relevance feedback models.
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de Groc, C., Tannier, X. (2012). Experiments on Pseudo Relevance Feedback Using Graph Random Walks. In: Calderón-Benavides, L., González-Caro, C., Chávez, E., Ziviani, N. (eds) String Processing and Information Retrieval. SPIRE 2012. Lecture Notes in Computer Science, vol 7608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34109-0_20
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DOI: https://doi.org/10.1007/978-3-642-34109-0_20
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