Modeling Syntactic Structures of Topics with a Nested HMM-LDA

• Main Author: JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: background functional words; hidden Markov models; latent Dirichlet allocation; syntactic structure modeling; text analysis; text mining; topic modeling method; topic-specific content words; Computer Sciences; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/351; http://dx.doi.org/10.1109/ICDM.2009.144
• Institution: Singapore Management University

Latent Dirichlet allocation (LDA) is a commonly used topic modeling method for text analysis and mining. Standard LDA treats documents as bags of words, ignoring the syntactic structures of sentences. In this paper, we propose a hybrid model that embeds hidden Markov models (HMMs) within LDA topics...