Abstract
A new method for information retrieval which is on the basis of language model with relative entropy and feedback is presented in this paper. The method builds a query language model and document language models respectively for the query and the documents. We rank the documents according to the relative entropies of the estimated document language models with respect to the estimated query language model. The feedback documents are used to estimate a query model by the approach that we assume that the feedback documents are generated by a combined model in which one component is the feedback document language model and the other is the collection language model. Experimental results show that the method is effective for feedback documents and performs better than the basic language modeling approach. The results also indicate that the performance of the method is sensitive to both the smoothing parameters and the interpolation coefficients used to estimate the values of the language models.
This research is supported by the Natural Science Foundation Program of the Henan Provincial Educational Department in China(200410464004) and the Science Research Foundation Program of Henan University of Science and Technology in China(2004ZY041).
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Huo, H., Feng, B. (2005). Retrieval Based on Language Model with Relative Entropy and Feedback. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_35
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DOI: https://doi.org/10.1007/11430919_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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