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- W4387169971 abstract "Transformer models have achieved state-of-the-art results for news classification tasks, but remain difficult to modify to yield the desired class probabilities in a multi-class setting. Using a neural topic model to create dense topic clusters helps with generating these class probabilities. The presented work uses the BERTopic clustered embeddings model as a preprocessor to eliminate documents that do not belong to any distinct cluster or topic. By combining the resulting embeddings with a Sentence Transformer fine-tuned with SetFit, we obtain a prompt-free framework that demonstrates competitive performance even with few-shot labeled data. Our findings show that incorporating BERTopic in the preprocessing stage leads to a notable improvement in the classification accuracy of news documents. Furthermore, our method outperforms hybrid approaches that combine text and images for news document classification." @default.
- W4387169971 created "2023-09-30" @default.
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- W4387169971 date "2023-01-01" @default.
- W4387169971 modified "2023-10-18" @default.
- W4387169971 title "Unsupervised Topic Modeling with BERTopic for Coarse and Fine-Grained News Classification" @default.
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- W4387169971 doi "https://doi.org/10.1007/978-3-031-43085-5_13" @default.
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