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Unified Neural Topic Model via Contrastive Learning and Term Weighting

Han, Sungwon / Shin, Mingi / Park, Sungkyu / Jung, Changwook / Cha, Meeyoung

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Abstract

Two types of topic modeling predominate: generative methods that employ probabilistic latent models and clustering methods that identify semantically coherent groups. This paper newly presents UTopic (Unified neural Topic model via contrastive learning and term weighting) that combines the advantages of these two types. UTopic uses contrastive learning and term weighting to learn knowledge from a pretrained language model and discover influential terms from semantically coherent clusters. Experiments show that the generated topics have a high-quality topic-word distribution in terms of topic coherence, outperforming existing baselines across multiple topic coherence measures. We demonstrate how our model can be used as an add-on to existing topic models and improve their performance.

Issue Date
2023-05
Publisher
Association for Computational Linguistics
URI
https://archives.kdischool.ac.kr/handle/11125/55015
10.18653/v1/2023.eacl-main.132
Conf. Name
The 17th Conference of the European Chapter of the Association for Computational Linguistics
Place
CT
Conference Date
2023-05-02
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