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This paper proposes an enhancement to the BERTopic model by incorporating pre-clustered knowledge to address feature sparsity in short text topic modeling. The method aims to improve clustering accuracy by reducing the impact of low-frequency words and optimizing similarity calculations. Empirical results on the StackOverflow dataset demonstrate that the proposed approach outperforms baseline models, achieving higher Macro-F1 scores.

Author

Azwa Safrina

Language

EN