Abstract
A new method for information retrieval based on relative entropy with different smoothing methods has been 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. While estimating a document language, the efficiency of the smoothing method is considered, we select three popular and relatively efficient methods to smooth the document 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 and performs better than the basic language modeling approach.
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., Liu, J., Feng, B. (2005). A New Method for Retrieval Based on Relative Entropy with Smoothing. In: Megiddo, N., Xu, Y., Zhu, B. (eds) Algorithmic Applications in Management. AAIM 2005. Lecture Notes in Computer Science, vol 3521. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11496199_21
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DOI: https://doi.org/10.1007/11496199_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26224-4
Online ISBN: 978-3-540-32440-9
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