AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon, Zhourong Chen

Last modified: 2016-02-21

Abstract


Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.

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