Hostname: page-component-8448b6f56d-xtgtn Total loading time: 0 Render date: 2024-04-23T17:34:43.314Z Has data issue: false hasContentIssue false
  • English
  • Français

Natural language processing and the Now-or-Never bottleneck

Published online by Cambridge University Press:  02 June 2016

Carlos Gómez-Rodríguez
Carlos Gómez-Rodríguez*
Affiliation:
LyS (Language and Information Society) Research Group, Departamento de Computación, Universidade da Coruña, Campus de Elviña, 15071, A Coruña, Spain. cgomezr@udc.eshttp://www.grupolys.org/~cgomezr
Get access Rights & Permissions [Opens in a new window]

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.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 39 , 2016 , e74
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bohnet, B. & Nivre, J. (2012) A transition-based system for joint part-of-speech tagging and labeled non-projective dependency parsing. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Jeju Island, Korea, July 12–14, 2012, ed. Tsujii, J., Henderson, J. & Pasca, M., pp. 1455–65. Association for Computational Linguistics.Google Scholar
Chen, D. & Manning, C. D. (2014) A fast and accurate dependency parser using neural networks. In: Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, Doha, Qatar, October 25–29, 2014, ed. Moschitti, A., Pang, B. & Daelemans, W., pp. 740–50. Association for Computational Linguistics.CrossRefGoogle Scholar
Choi, J. D. & McCallum, A. (2013) Transition-based dependency parsing with selectional branching. In: Proceedings of the 51 st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria, August 4–9, 2013, ed. Fung, P. & Poesio, M., pp. 1052–62. Association for Computational Linguistics.Google Scholar
Dyer, C., Ballesteros, M., Ling, W., Matthews, A. & Smith, N. (2015) Transition-based dependency parsing with stack long short-term memory. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Beijing, China, July 26–31, 2015, ed. Zong, C. & Strube, M., pp. 334–43. Association for Computational Linguistics.CrossRefGoogle Scholar
Gómez-Rodríguez, C. & Nivre, J. (2013) Divisible transition systems and multiplanar dependency parsing. Computational Linguistics 39(4):799–45.CrossRefGoogle Scholar
Gómez-Rodríguez, C., Sartorio, F. & Satta, G. (2014) A polynomial-time dynamic oracle for non-projective dependency parsing. In: Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing, Doha, Qatar, October 25–29, 2014, ed. Moschitti, A., Pang, B. & Daelemans, W., pp. 917–27. Association for Computational Linguistics.CrossRefGoogle Scholar
Hatori, J., Matsuzaki, T., Miyao, Y. & Tsujii, J. (2012) Incremental joint approach to word segmentation, POS tagging, and dependency parsing in Chinese. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jeju Island, Korea, July 8–14, 2012, ed. Li, H., Lin, C-Y., Osborne, M., Lee, G. G. & Park, J. C., pp. 1216–24. Association for Computational Linguistics.Google Scholar
Nivre, J. (2003) An efficient algorithm for projective dependency parsing. In: Proceedings of the 8th International Workshop on Parsing Technologies (IWPT 03), Nancy, France, April 23–25, 2003, ed. Bunt, H. & Noord, G. van, pp. 149–60. Association for Computational Linguistics.Google Scholar
Nivre, J. (2004) Incrementality in deterministic dependency parsing. In: Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together, ed. Keller, F., Clark, S., Crocker, M. & Steedman, M., pp. 50–57. Association for Computational Linguistics.CrossRefGoogle Scholar
Nivre, J. (2008) Algorithms for deterministic incremental dependency parsing. Computational Linguistics 34(4):513–53.CrossRefGoogle Scholar
Nivre, J., Hall, J., Nilsson, J., Chanev, A., Eryigit, G., Kübler, S., Marinov, S. & Marsi, E. (2007) MaltParser: A language-independent system for data-driven dependency parsing. Natural Language Engineering 13(2):95–35.CrossRefGoogle Scholar
Zhang, Y. & Clark, S. (2011) Syntactic processing using the generalized perceptron and beam search. Computational Linguistics 37(1):105–51.CrossRefGoogle Scholar