Evaluating topic coherence measures
Machine Learning
2014-03-26 v1 Computation and Language
Information Retrieval
Abstract
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.
Cite
@article{arxiv.1403.6397,
title = {Evaluating topic coherence measures},
author = {Frank Rosner and Alexander Hinneburg and Michael Röder and Martin Nettling and Andreas Both},
journal= {arXiv preprint arXiv:1403.6397},
year = {2014}
}
Comments
This work has been presented at the "Topic Models: Computation, Application and Evaluation" workshop at the "Neural Information Processing Systems" conference 2013