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| Model tematyczny LDA× | Klasyfikacja oparta na BERT× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2003 | 2019 |
| Twórca≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. | Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language) |
| Typ≠ | Probabilistic generative topic model | Pre-trained language model with fine-tuning |
| Źródło pierwotne≠ | Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗ | 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 ↗ |
| Inne nazwy | LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words. | 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. |
| ScholarGateZbiór danych ↗ |
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