Integrating Conceptual Knowledge into Relevance Models: A Model and Estimation Method

We address the issue of combining explicit background knowledge with pseudo-relevance feedback from within a document collection. To this end, we use document-level annotations in tandem with generative language models to generate terms from pseudo-relevant documents and bias the probability estimates of expansion terms in a principled manner. By applying the knowledge inherent in document annotations, we aim to control query drift and reap the benefits of automatic query expansion in terms of recall without losing precision. We consider the parameters which are associated with our modeling and describe ways of estimating these automatically. We then evaluate our modeling and estimation methods on two test collections, both provided by the TREC Genomics track.

  • [PDF] E. Meij and M. de Rijke, “Integrating Conceptual Knowledge into Relevance Models: A Model and Estimation Method,” in Proceedings of the 1st international conference on theory of information retrieval, 2007.
    [Bibtex]
    @inproceedings{ICTIR:2007:meij,
    Author = {E. Meij and de Rijke, M.},
    Booktitle = {Proceedings of the 1st International Conference on Theory of Information Retrieval},
    Date-Added = {2011-10-12 18:31:55 +0200},
    Date-Modified = {2012-10-30 08:50:30 +0000},
    Series = {ICTIR 2007},
    Title = {{Integrating Conceptual Knowledge into Relevance Models: A Model and Estimation Method}},
    Year = {2007}}

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