Eisenstein et al 2011: Sparse Additive Generative Models of Text
Contents
Citation
Sparse Additive Generative Models of Text. Eisenstein, Ahmed and Xing. Proceedings of ICML 2011.
Online version
Summary
This recent paper presents sparse learning and additive generative modeling approaches for Topic modeling. This is an important alternative approach to Latent Dirichlet Allocation (LDA) where sparsity and log-space additive modeling are NOT considered or introduced.
Brief Description of the method
This paper first formulates the problem as xxx, then discusses the use of and the use of xxx to. In this section, we will first summarize the method they use
Dataset and Experiment Settings
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Experimental Results
The authors performed three major experiments. The first experiment is the . The second experiment explores.
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Related Papers
This paper is related to many papers in three dimensions.
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