手法を比較

	弱教師ありLSTM	リカレントニューラルネットワーク (RNN)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2016–2018	1986–1990
提唱者	Ratner et al. (data programming framework); Hochreiter & Schmidhuber (LSTM backbone)	Rumelhart, D. E.; Elman, J. L.
種類	Weakly supervised sequence model	Sequential neural network
原典	Ratner, A., De Sa, C., Wu, S., Selsam, D., & Re, C. (2016). Data Programming: Creating Large Training Sets, Quickly. Advances in Neural Information Processing Systems (NeurIPS), 29.	Elman, J. L. (1990). Finding structure in time. Cognitive Science, 14(2), 179–211.
別名	WS-LSTM, noisy-label LSTM, distant-supervision LSTM, data-programming LSTM	RNN, Elman network, Jordan network, simple recurrent network
関連	6	3
概要	Weakly supervised LSTM trains a Long Short-Term Memory network on sequence data where clean, manually annotated labels are scarce or absent. Instead, multiple imperfect label sources — heuristic rules, distant supervision, crowdsourcing, or programmatic labeling functions — are combined to produce probabilistic training labels, which are then used to supervise the LSTM. This allows scalable training on large unlabeled corpora without exhaustive human annotation.	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.