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Machine learningDeep learning / NLP / CV

Klasifikasi BERT Berbantukan Pengawasan Lemah

Klasifikasi BERT berbantukan pengawasan lemah menyesuaikan BERT kepada tugasan klasifikasi teks apabila hanya label yang hingar, heuristik, atau dijana secara programatik tersedia berbanding anotasi manusia yang bersih. Ia menggabungkan rangka kerja pengawasan lemah — seperti fungsi pelabelan dan pengaturcaraan data — dengan perwakilan bahasa pra-latih BERT untuk mencapai klasifikasi yang teguh tanpa pelabelan tangan yang mahal.

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Sumber

  1. Meng, Y., Zhang, Y., Huang, J., Xiong, C., Ji, H., Zhang, C., & Han, J. (2020). Text Classification Using Label Names Only: A Language Model Self-Training Approach. Proceedings of EMNLP 2020, 9006–9017. link
  2. Ratner, A., Bach, S. H., Ehrenberg, H., Fries, J., Wu, S., & Re, C. (2017). Snorkel: Rapid Training Data Creation with Weak Supervision. Proceedings of the VLDB Endowment, 11(3), 269–282. DOI: 10.14778/3157794.3157797

Cara memetik halaman ini

ScholarGate. (2026, June 3). Weakly Supervised BERT-based Text Classification. ScholarGate. https://scholargate.app/ms/deep-learning/weakly-supervised-bert-based-classification

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ScholarGateWeakly supervised BERT-based classification (Weakly Supervised BERT-based Text Classification). Dicapai 2026-06-15 daripada https://scholargate.app/ms/deep-learning/weakly-supervised-bert-based-classification · Set data: https://doi.org/10.5281/zenodo.20539026