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The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing.
Finding structure in time
Jeffrey L Elman · 1990
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Learning long-term dependencies with gradient descent is difficult
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Learning task-dependent distributed representations by backpropagation through structure
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Long short-term memory
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Bidirectional recurrent neural networks
Mike Schuster and Kuldip K Paliwal · 1997
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Learning to forget: Continual prediction with lstm
Felix A Gers, Jürgen Schmidhuber, and Fred Cummins · 2000
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Recurrent neural network based language model
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Rectified linear units improve restricted boltzmann machines
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Learning continuous phrase representations and syntactic parsing with recursive neural networks
Richard Socher, Christopher D Manning, and Andrew Y Ng · 2010
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Natural language processing (almost) from scratch
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Adaptive subgradient methods for online learning and stochastic optimization
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On the difficulty of training recurrent neural networks
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Recursive deep models for semantic compositionality over a sentiment treebank
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On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
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Convolutional neural network architectures for matching natural language sentences
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A convolutional neural network for modelling sentences
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Semantic compositionality through recursive matrix-vector spaces
Richard Socher, Brody Huval, Christopher D Manning, and Andrew Y Ng · 2012
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Baselines and bigrams: Simple, good sentiment and topic classification
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Convolutional neural networks for sentence classification
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Distributed representations of sentences and documents
Quoc V Le and Tomas Mikolov · 2014
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Neural machine translation by jointly learning to align and translate
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