@inproceedings{ding-etal-2018-coherence,
title = "Coherence-Aware Neural Topic Modeling",
author = "Ding, Ran and
Nallapati, Ramesh and
Xiang, Bing",
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/D18-1096",
doi = "10.18653/v1/D18-1096",
pages = "830--836",
abstract = "Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.",
}
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<abstract>Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.</abstract>
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%0 Conference Proceedings
%T Coherence-Aware Neural Topic Modeling
%A Ding, Ran
%A Nallapati, Ramesh
%A Xiang, Bing
%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 ding-etal-2018-coherence
%X Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.
%R 10.18653/v1/D18-1096
%U https://aclanthology.org/D18-1096
%U https://doi.org/10.18653/v1/D18-1096
%P 830-836
Markdown (Informal)
[Coherence-Aware Neural Topic Modeling](https://aclanthology.org/D18-1096) (Ding et al., EMNLP 2018)
ACL
- Ran Ding, Ramesh Nallapati, and Bing Xiang. 2018. Coherence-Aware Neural Topic Modeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 830–836, Brussels, Belgium. Association for Computational Linguistics.