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Bianchi, F., Terragni, S. and Hovy, D. (2021) Pre-Training Is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Online, 1-6 August 2021, 759-766.
https://doi.org/10.18653/v1/2021.acl-short.96

has been cited by the following article:

• TITLE: Enhancing BERTopic with Pre-Clustered Knowledge: Reducing Feature Sparsity in Short Text Topic Modeling

  AUTHORS: Qian Wang, Biao Ma

  KEYWORDS: Topic Model, BERTopic, Short Text, Feature Sparsity, Cluster

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.12 No.4, November 21, 2024

  ABSTRACT: Modeling topics in short texts presents significant challenges due to feature sparsity, particularly when analyzing content generated by large-scale online users. This sparsity can substantially impair semantic capture accuracy. We propose a novel approach that incorporates pre-clustered knowledge into the BERTopic model while reducing the l2 norm for low-frequency words. Our method effectively mitigates feature sparsity during cluster mapping. Empirical evaluation on the StackOverflow dataset demonstrates that our approach outperforms baseline models, achieving superior Macro-F1 scores. These results validate the effectiveness of our proposed feature sparsity reduction technique for short-text topic modeling.