方法对比
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| NMF 主题模型× | [需翻译标题：BERT-based Classification...]× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1999 | 2019 |
| 提出者≠ | Lee, D. D. & Seung, H. S. | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| 类型≠ | Matrix factorization / unsupervised topic model | Pre-trained language model with fine-tuning |
| 开创性文献≠ | Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791. DOI ↗ | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics. DOI ↗ |
| 别名 | NMF, Non-negative Matrix Factorization, NMF for Topic Modeling, NNMF Topic Model | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| 相关 | 4 | 4 |
| 摘要≠ | Non-negative Matrix Factorization (NMF) is an unsupervised matrix decomposition method that discovers latent topics in a text corpus by factoring a document-term matrix into two non-negative matrices — one encoding topic-word weights, the other document-topic weights. The non-negativity constraint yields parts-based, additive representations that tend to produce clean, interpretable topics. | BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data. |
| ScholarGate数据集 ↗ |
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