Hierarchical Topic Model Research Articles

Semantic multi-grain mixture topic model for text analysis

Granular topic extraction and modeling are fundament tasks in text analysis. Hierarchical topic clustering algorithms and hierarchical topic models are usually employed for these purposes. However, it is difficult to make a clear distinguish between each pair of hierarchical topics from the semantic granularity point of view. STG (semantic topic granularity) is proposed to indicate the details degree of topic description, and aim at providing discrimination for topics from semantic aspect. A new model, mgMTM (multi-grain mixture topic model) based on STG is then proposed to model grain topics. DCT (discrete cosine transform) is employed to provide a mechanism for computing STG, extracting grain topics and learning mgMTM. Experiments on real world datasets show that the proposed model has lower perplexity score than that of LDA model and thus has better generalization performance in describing text. Experiments also show that the description of the extracted grain topics can be well explained with respect to a dataset including topics about recent global financial crisis.

A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases

Political scientists lack methods to efficiently measure the priorities political actors emphasize in statements. To address this limitation, I introduce a statistical model that attends to the structure of political rhetoric when measuring expressed priorities: statements are naturally organized by author. The expressed agenda model exploits this structure to simultaneously estimate the topics in the texts, as well as the attention political actors allocate to the estimated topics. I apply the method to a collection of over 24,000 press releases from senators from 2007, which I demonstrate is an ideal medium to measure how senators explain their work in Washington to constituents. A set of examples validates the estimated priorities and demonstrates their usefulness for testing theories of how members of Congress communicate with constituents. The statistical model and its extensions will be made available in a forthcoming free software package for the R computing language.

Hierarchical topic modeling with nested hierarchical Dirichlet process

This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonparametric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as well as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more fine-grained topic relationships compared to the hierarchical latent Dirichlet allocation model.