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Recurrent neural network grammars (RNNGs) are generative models of (tree,string) pairs that rely on neural networks to evaluate derivational choices.
Augmented transition networks as psychological models of sentence comprehension
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A Computational Theory of Human Linguistic Processing: Memory Limitations and Processing Breakdown
Edward Gibson. 1991 · 1991
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Event-related brain potentials elicited by syntactic anomaly
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David J. Townsend and Thomas G. Bever. 2001 · 2001
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Eric Maris and Robert Oostenveld. 2007 · 2007
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Imaging neural correlates of syntactic complexity in a naturalistic context
Asaf Bachrach. 2008 · 2008
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Deriving lexical and syntactic expectation-based measures for psycholinguistic modeling via incremental top-down parsing
Brian Roark, Asaf Bachrach, Carlos Cardenas, and Christophe Pallier. 2009 · 2009
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Frank Keller. 2010 · 2010
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Marisa Ferrara Boston, John T. Hale, Shravan Vasishth, and Reinhold Kliegl. 2011 · 2011
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Alex Graves. 2012 · 2012
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Tomáš Mikolov. 2012 · 2012
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Vera Demberg, Frank Keller, and Alexander Koller. 2013 · 2013
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Colin Phillips. 2013 · 2013
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Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
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Electrophysiology reveals the neural dynamics of naturalistic auditory language processing: event-related potentials reflect continuous model updates
Phillip M. Alday, Matthias Schlesewsky, and Ina Bornkessel-Schlesewsky. 2017 · 2017
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MEG evidence for incremental sentence composition in the anterior temporal lobe
Jonathan R. Brennan and Liina Pylkkänen. 2017 · 2017
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A neurocomputational model of the N400 and the P600 in language processing
Harm Brouwer, Matthew W. Crocker, Noortje J. Venhuizen, and John C. J. Hoeks. 2017 · 2017
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Daniel Fried, Mitchell Stern, and Dan Klein. 2017 · 2017
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Approximations of predictive entropy correlate with reading times
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Effective inference for generative neural parsing
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Hierarchical structure guides rapid linguistic predictions during naturalistic listening
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