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Li, B., Zhou, H., He, J., Wang, M., Yang, Y. and Li, L. (2020) On the Sentence Embeddings from Pre-Trained Language Models. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online, 16-20 November 2020, 9119-9130.
https://doi.org/10.18653/v1/2020.emnlp-main.733

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.