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Reimers, N. and Gurevych, I. (2019) Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, 3-7 November 2019, 3982-3992.
https://doi.org/10.18653/v1/d19-1410

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.