@inproceedings{bhatia-etal-2018-topic,
title = "Topic Intrusion for Automatic Topic Model Evaluation",
author = "Bhatia, Shraey and
Lau, Jey Han and
Baldwin, Timothy",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1098",
doi = "10.18653/v1/D18-1098",
pages = "844--849",
abstract = "Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task {---} the task of guessing an outlier topic given a document and a few topics {---} and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.",
}
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<abstract>Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task — the task of guessing an outlier topic given a document and a few topics — and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.</abstract>
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%0 Conference Proceedings
%T Topic Intrusion for Automatic Topic Model Evaluation
%A Bhatia, Shraey
%A Lau, Jey Han
%A Baldwin, Timothy
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F bhatia-etal-2018-topic
%X Topic coherence is increasingly being used to evaluate topic models and filter topics for end-user applications. Topic coherence measures how well topic words relate to each other, but offers little insight on the utility of the topics in describing the documents. In this paper, we explore the topic intrusion task — the task of guessing an outlier topic given a document and a few topics — and propose a method to automate it. We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.
%R 10.18653/v1/D18-1098
%U https://aclanthology.org/D18-1098
%U https://doi.org/10.18653/v1/D18-1098
%P 844-849
Markdown (Informal)
[Topic Intrusion for Automatic Topic Model Evaluation](https://aclanthology.org/D18-1098) (Bhatia et al., EMNLP 2018)
ACL
- Shraey Bhatia, Jey Han Lau, and Timothy Baldwin. 2018. Topic Intrusion for Automatic Topic Model Evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 844–849, Brussels, Belgium. Association for Computational Linguistics.