COLBERT: A scoring based graphical model for expert identification

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Abstract

In recent years a number of graphical models have been proposed for Topic discovery in various contexts and network analysis. However there is one class of document corpus, documents with ratings, where the problem of topic discovery has not been explored in much detail. In such document corpuses reviews and ratings of documents in addition to the documents themselves are also available. In this paper we address the problem of discovery of latent structures in document-review corpus which can then be used to construct a social network of experts. We present a graphical model COLBERT that automatically discovers latent topics based on the contents of the document, the review of the document and the ratings of the review. © Springer-Verlag Berlin Heidelberg 2010.

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Ahmad, M. A., & Zhao, X. (2010). COLBERT: A scoring based graphical model for expert identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6007 LNCS, pp. 180–188). https://doi.org/10.1007/978-3-642-12079-4_24

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