Modeling Syntactic Structures of Topics with a Nested HMM-LDA
Conference Proceeding Article
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 to jointly model both the topics and the syntactic structures within each topic. Our model is general and subsumes standard LDA and HMM as special cases. Compared with standard LDA and HMM, our model can simultaneously discover both topic-specific content words and background functional words shared among topics. Our model can also automatically separate content words that play different roles within a topic. Using perplexity as evaluation metric, our model returns lower perplexity for unseen test documents compared with standard LDA, which shows its better generalization power than LDA.
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
Information Systems and Management; Data Management and Analytics
9th IEEE International Conference on Data Mining
City or Country
Modeling Syntactic Structures of Topics with a Nested HMM-LDA. (2009). 9th IEEE International Conference on Data Mining. 824-829. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/351