手法を比較

	弱教師ありリカレントニューラルネットワーク	Long Short-Term Memory (LSTM)
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2009–2016	1997
提唱者	Broadly attributed to the weak supervision / distant supervision research community (Mintz et al., 2009; Ratner et al., 2016)	Hochreiter, S. & Schmidhuber, J.
種類	Supervised learning under noisy or incomplete labels	Recurrent neural network with gated memory cells
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.	Hochreiter, S. & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
別名	WS-RNN, distantly supervised RNN, noise-tolerant RNN, weakly supervised sequence model	LSTM, LSTM network, LSTM-RNN, long short-term memory RNN
関連	5	4
概要	A weakly supervised RNN trains a recurrent neural network on sequences whose labels come from imperfect sources — heuristic rules, distant supervision, crowdsourcing, or generative label models — rather than expensive expert annotation. This lets researchers exploit large unlabeled corpora for sequential tasks such as text classification, named entity recognition, or time-series prediction when fully annotated data is scarce or costly.	Long Short-Term Memory (LSTM) is a gated recurrent neural network architecture introduced by Hochreiter and Schmidhuber in 1997. It was designed to learn dependencies across long sequences by using dedicated memory cells and three learned gates — forget, input, and output — that control what information is retained, updated, or passed forward at each time step.