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Sequential Latent Dirichlet Allocation: Discover Underlying Topic Structures within a Document

Du, Lan; Buntine, Wray; Jin, Huidong (Warren)

Description

Understanding how topics within a document evolve over its structure is an interesting and important problem. In this paper, we address this problem by presenting a novel variant of Latent Dirichlet Allocation (LDA): Sequential LDA (SeqLDA). This variant

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
URI: http://hdl.handle.net/1885/27186
Source: IEEE International Conference on Data Mining (ICDM 2010) proceedings
DOI: 10.1109/ICDM.2010.51

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