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Natural language processing and the Now-or-Never bottleneck

  • Carlos Gómez-Rodríguez (a1)
Abstract
Abstract

Researchers, motivated by the need to improve the efficiency of natural language processing tools to handle web-scale data, have recently arrived at models that remarkably match the expected features of human language processing under the Now-or-Never bottleneck framework. This provides additional support for said framework and highlights the research potential in the interaction between applied computational linguistics and cognitive science.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

C. Gómez-Rodríguez & J. Nivre (2013) Divisible transition systems and multiplanar dependency parsing. Computational Linguistics 39(4):799–45.

J. Nivre (2008) Algorithms for deterministic incremental dependency parsing. Computational Linguistics 34(4):513–53.

Y. Zhang & S. Clark (2011) Syntactic processing using the generalized perceptron and beam search. Computational Linguistics 37(1):105–51.

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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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