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- W2963747179 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." @default.
- W2963747179 created "2019-07-30" @default.
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- W2963747179 date "2016-02-21" @default.
- W2963747179 modified "2023-10-14" @default.
- W2963747179 title "Progressive EM for Latent Tree Models and Hierarchical Topic Detection" @default.
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- W2963747179 doi "https://doi.org/10.1609/aaai.v30i1.10196" @default.
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