手法を比較

	リカレントニューラルネットワーク (RNN)	BERTベースの分類
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	1986–1990	2019
提唱者	Rumelhart, D. E.; Elman, J. L.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
種類	Sequential neural network	Pre-trained language model with fine-tuning
原典	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.	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.
別名	RNN, Elman network, Jordan network, simple recurrent network	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
関連	3	4
概要	A Recurrent Neural Network (RNN) is a class of neural network designed to process sequential data by maintaining a hidden state that carries information across time steps. Introduced in its modern form by Rumelhart et al. (1986) and further shaped by Elman (1990), RNNs became the dominant architecture for sequence modelling in NLP, speech, and time-series analysis before the rise of attention-based models.	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.