Sungwon Han
2023
Unified Neural Topic Model via Contrastive Learning and Term Weighting
Sungwon Han
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Mingi Shin
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Sungkyu Park
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Changwook Jung
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Meeyoung Cha
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
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.
2020
A Risk Communication Event Detection Model via Contrastive Learning
Mingi Shin
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Sungwon Han
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Sungkyu Park
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Meeyoung Cha
Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
This paper presents a time-topic cohesive model describing the communication patterns on the coronavirus pandemic from three Asian countries. The strength of our model is two-fold. First, it detects contextualized events based on topical and temporal information via contrastive learning. Second, it can be applied to multiple languages, enabling a comparison of risk communication across cultures. We present a case study and discuss future implications of the proposed model.
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