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Reading Tea Leaves: How Humans Interpret Topic Models

Annual Conference on Neural Information Processing Systems, (2009)

Cited: 2054|Views16
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Abstract

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Practitioners typically assume that the latent space is semantically meaningful. It is used to check models, summarize the corpus, and guide exploration of i...More

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Author
C Wang
C Wang
JL Boyd-Graber
JL Boyd-Graber
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