Abstract
Most sentences expressed in a natural language is ambiguous. However, human beings effortlessly understand the intended message of the sentence even when a computer program finds out countless possible interpretations. If we want to create a computer program that understands a natural language in the same way as human beings do, a promising way would be implementing a human-like mechanism of sentence processing instead of implementing a “list exhaustively then select” method. By the way, it is highly likely that human’s language ability is realized mostly by the cerebral cortex, and recent neuroscientific studies hypothesize that the cerebral cortex works as a Bayesian network. Then it should be possible to reproduce human’s language ability using a Bayesian network. Based on this idea, we implemented a syntactic parser using a restricted quasi Bayesian network, which is a prototyping tool for creating models of cerebral cortical areas. The parser analyzes a sequence of syntactic categories based on a subset of combinatory categorial grammar. We confirmed that the parser correctly parsed grammatical sequences and rejected ungrammatical sequences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chikkerur, S., Serre, T., Tan, C., Poggio, T.: What and where: a Bayesian inference theory of attention. Vis. Res. 50(22), 2233–2247 (2010)
Dura-Bernal, S., Wennekers, T., Denham, S.L.: Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation. PLoS One 7(11), e48216 (2012)
George, D., Hawkins, J.: A hierarchical Bayesian model of invariant pattern recognition in the visual cortex. In: 2005 International Joint Conference on Neural Networks (IJCNN) (2005)
Hosoya, H.: Multinominal Bayesian learning for modeling classical and nonclassical receptive field properties. Neural Comput. 24(8), 2119–2150 (2012)
Ichisugi, Y.: The cerebral cortex model that self-organizes conditional probability tables and executes belief propagation. In: 2007 International Joint Conference on Neural Networks (IJCNN) (2007)
Ichisugi, Y.: Recognition model of cerebral cortex based on approximate belief revision algorithm. In: 2011 International Joint Conference on Neural Networks (IJCNN) (2011)
Kemmerer, D.: Cognitive Neuroscience of Language. Psychology Press, Abingdon (2015)
Lee, T.S., Mumford, D.: Hierarchical Bayesian inference in the visual cortex. J. Opt. Soc. Am. A 20(7), 1434–1448 (2003)
Litvak, S., Ullman, S.: Cortical circuitry implementing graphical models. Neural Comput. 21(11), 3010–3056 (2009)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
Pitkow, X., Angelaki, D.E.: Inference in the brain: statistics flowing in redundant population codes. Neuron 94(5), 943–953 (2017)
Raju, R.V., Pitkow, X.: Inference by reparameterization in neural population codes. In: Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 5–10 December 2016, Barcelona, Spain, pp. 2029–2037 (2016)
Rao, R.P.: Bayesian inference and attention modulation in the visual cortex. Neuroreport 16(16), 1843–1848 (2005)
Röhrbein, F., Eggert, J., Körner, E.: Bayesian columnar networks for grounded cognitive system. In: Proceedings of the 30th Annual Conference of the Cognitive Science Society, pp. 1423–1428 (2008)
Steedman, M.: The Syntactic Process. MIT Press, Cambridge (2000)
Takahashi, N., Ichisugi, Y.: Restricted quasi Bayesian networks as a prototyping tool for computational models of individual cortical areas. In: Proceedings of Machine Learning Research, PMLR, vol. 73 (2017)
Acknowledgement
This paper is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Takahashi, N., Ichisugi, Y. (2019). Toward Human-Like Sentence Interpretation—a Syntactic Parser Implemented as a Restricted Quasi Bayesian Network—. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2018. BICA 2018. Advances in Intelligent Systems and Computing, vol 848. Springer, Cham. https://doi.org/10.1007/978-3-319-99316-4_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-99316-4_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99315-7
Online ISBN: 978-3-319-99316-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)