Abstract
A common limitation of many language modeling approaches is that retrieval scores are mainly based on exact matching of terms in the queries and documents, ignoring the semantic relations among terms. Latent Dirichlet Allocation (LDA) is an approach trying to capture the semantic dependencies among words. However, using as document representation, LDA has no successful applications in information retrieval (IR). In this paper, we propose a single-document-based LDA (SLDA) document model for IR. The proposed work has been evaluated on four TREC collections, which shows that SLDA document modeling method is comparable to the state-of-the-art language modeling approaches, and it’s a novel way to use LDA model to improve retrieval performance.
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Ma, D., Rao, L., Wang, T. (2011). An Empirical Study of SLDA for Information Retrieval. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_8
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DOI: https://doi.org/10.1007/978-3-642-25631-8_8
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