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2014

Evaluating topic coherence measures

Rosner, Frank, Hinneburg, Alexander, Röder, Michael et al.

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Topic models extract representative word sets - called topics - from word counts in documents without requiring any semantic annotations.

Topics are not guaranteed to be well interpretable, therefore, coherence measures have been proposed to distinguish between good and bad topics.
Studies of topic coherence so far are limited to measures that score pairs of individual words.
For the first time, we include coherence measures from scientific philosophy that score pairs of more complex word subsets and apply them to topic scoring.

Evaluating topic coherence measures · Around

Built on

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J.R. Landis and G.G. Koch · 1977
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Paradigms of artificial intelligence programming : case studies in Common LISP
P. Norvig · 1992
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Mallet: A machine learning for language toolkit. 2002
A. McCallum · 2002
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Latent dirichlet allocation
D.M. Blei, A.Y. Ng, and M.I. Jordan · 2003
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Bayesian Epistemology
L. Bovens and S. Hartmann · 2003
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