Abstract
This paper describes a new relevance feedback (RF) method that uses latent topic information extracted from target documents.In the method, we extract latent topics of the target documents by means of latent Dirichlet allocation (LDA) and expand the initial query by providing the topic distributions of the documents retrieved at the first search. We conduct experiments for retrieving information by our proposed method and confirm that our proposed method is especially useful when the precision of the first search is low. Furthermore, we discuss the cases where RF based on latent topic information and RF based on surface information, i.e., word frequency, work well, respectively.
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Serizawa, M., Kobayashi, I. (2013). A Study on Query Expansion Based on Topic Distributions of Retrieved Documents. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_31
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DOI: https://doi.org/10.1007/978-3-642-37256-8_31
Publisher Name: Springer, Berlin, Heidelberg
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