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Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality (2014)
| Venue: | In Proc. of the Europ. Chap. of the Assoc. for |
| Citations: | 9 - 0 self |
BibTeX
@INPROCEEDINGS{Lau14machinereading,
author = {Jey Han Lau and David Newman and Timothy Baldwin},
title = {Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality},
booktitle = {In Proc. of the Europ. Chap. of the Assoc. for},
year = {2014}
}
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Abstract
Topic models based on latent Dirichlet al-location and related methods are used in a range of user-focused tasks including doc-ument navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two tasks of automatic evaluation of single topics and automatic evaluation of whole topic models, and provide recom-mendations on the best strategy for per-forming the two tasks, in addition to pro-viding an open-source toolkit for topic and topic model evaluation. 1
