Optimizing Semantic Coherence in Topic Models

Abstract

Latent variable models have the potential to add value to large document collections by discovering interpretable, low-dimensional subspaces. In order for people to use such models, however, they must trust them. Unfortunately, typical dimensionality reduction methods for text, such as latent Dirichlet allocation, often produce low-dimensional subspaces (topics) that are obviously flawed to human domain experts. The contributions of this paper are threefold: (1) An analysis of the ways in which topics can be flawed; (2) an automated evaluation metric for identifying such topics that does not rely on human annotators or reference collections outside the training data; (3) a novel statistical topic model based on this metric that significantly improves topic quality in a large-scale document collection from the National Institutes of Health (NIH).

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@inproceedings{Mimno2011OptimizingSC, title={Optimizing Semantic Coherence in Topic Models}, author={David M. Mimno and Hanna M. Wallach and Edmund M. Talley and Miriam Leenders and Andrew McCallum}, booktitle={EMNLP}, year={2011} }