Title

Modeling Syntactic Structures of Topics with a Nested HMM-LDA

Publication Type

Conference Proceeding Article

Publication Date

12-2009

Abstract

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.

Keywords

background functional words, hidden Markov models, latent Dirichlet allocation, syntactic structure modeling, text analysis, text mining, topic modeling method, topic-specific content words

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing

Research Areas

Information Systems and Management; Data Management and Analytics

Publication

9th IEEE International Conference on Data Mining

First Page

824

Last Page

829

ISBN

9780769538952

Identifier

10.1109/ICDM.2009.144

Publisher

IEEE

City or Country

Miami, FL

Additional URL

http://dx.doi.org/10.1109/ICDM.2009.144