Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

December 22, 2010

Reading Tea Leaves: How Humans Interpret Topic Models

Filed under: Latent Dirichlet Allocation (LDA),Topic Models (LDA) — Patrick Durusau @ 9:12 am

Reading Tea Leaves: How Humans Interpret Topic Models Authors: Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei

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 its contents. However, whether the latent space is interpretable is in need of quantitative evaluation. In this paper, we present new quantitative methods for measuring semantic meaning in inferred topics. We back these measures with large-scale user studies, showing that they capture aspects of the model that are undetected by previous measures of model quality based on held-out likelihood. Surprisingly, topic models which perform better on held-out likelihood may infer less semantically meaningful topics.

Read the article first but then see the LingPipe Blog review of the same.

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