Natural Language Understanding: Methodological Conceptualization
This article contains the results of a theoretical analysis of the phenomenon of natural language understanding (NLU), as a methodological problem. The combination of structural-ontological and informational-psychological approaches provided an opportunity to describe the subject matter field of NLU, as a composite function of the mind, which systemically combines the verbal and discursive structural layers. In particular, the idea of NLU is presented, on the one hand, as the relation between the discourse of a specific speech message and the meta-discourse of a language, in turn, activated by the need-motivational factors. On the other hand, it is conceptualized as a process with a specific structure of information metabolism, the study of which implies the necessity to differentiate the affective (emotional) and need-motivational influences on the NLU, as well as to take into account their interaction. At the same time, the hypothesis about the influence of needs on NLU under the scenario similar to the pattern of Yerkes-Dodson is argued. And the theoretical conclusion that emotions fulfill the function of the operator of the structural features of the information metabolism of NLU is substantiated. Thus, depending on the modality of emotions in the process of NLU, it was proposed to distinguish two scenarios for the implementation of information metabolism - reduction and synthetic. The argument in favor of the conclusion about the productive and constitutive role of emotions in the process of NLU is also given.
Carandini, M., & Heeger, D.J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1), 51–62. https://doi.org/10.1038/nrn3136
Crocker, M.W. (1996). Computational Psycholinguistics an Interdisciplinary Approach to the Study of Language. Springer, Dordrecht.
Foucault, M. (1972). Archaeology of knowledge and the discourse on language. New York: Pantheon.
Friederici, A.D. (2012). The cortical language circuit: From auditory perception to sentence comprehension. Trends in Cognitive Sciences, 16(5), 262–268. https://doi.org/10.1016/j.tics.2012.04.001
Giraud, A.-L., & Poeppel, D. (2012). Cortical oscillations and speech processing: Emerging computational principles and operations. Nature Neuroscience, 15(4), 511–517. https://doi.org/10.1038/nn.3063
Hagoort, P. (2005). On broca, brain, and binding: A new framework. Trends in Cognitive Sciences, 9(9), 416–423. https://doi.org/10.1016/j.tics.2005.07.004
Hale, J. (2001). A probabilistic earley parser as a psycholinguistic model. Proceedings of the Second Meeting of the North American Chapter of the Association for Computational Linguistics on Language Technologies (pp. 1–8). Stroudsburg, PA: Association for Computational Linguistics. https://doi.org/10.3115/1073336.1073357
Harley, T.A. (2005). The Psychology of Language From Data to Theory (2nd ed.). Taylor & Francis.
Huettig, F. (2015). Four central questions about prediction in language processing. Brain Research, 1626, 118–135. https://doi.org/10.1016/j.brainres.2015.02.014
Leontev, A.N. (1978). Activity, Consciousness, and Personality. Prentice-Hall https://www.marxists.org/archive/leontev/works/1978/index.htm
Lewis, R.L., Vasishth, S., & Van Dyke, J.A. (2006). Computational principles of working memory in sentence comprehension. Trends in Cognitive Sciences, 10(10), 447–454. https://doi.org/10.1016/j.tics.2006.08.007
Martin, A.E. (2016). Language processing as cue integration: Grounding the psychology of language in perception and neurophysiology. Frontiers in Psychology, 7, Article ID 120. https://doi.org/10.3389/fpsyg.2016.00120
McElree, B. (2000). Sentence comprehension is mediated by content-addressable memory structures. Journal of Psycholinguistic Research, 29(2), 111–123. https://doi.org/10.1023/A:1005184709695
Rubinstein, S.L. (1999). Fundamentals of General Psychology. Russia: Piter Com.
Russell, S.J., & Norvig, P. (2010). Artificial intelligence: a modern approach (3rd ed.). Upper Saddle River: Prentice Hall.
Shymko, V. (2018a). In Pursuit of the Functional Definition of a Mind: the Inevitability of the Language Ontology. Psycholinguistics, 23(1), 327–346. https://doi.org/10.5281/zenodo.1211593
Shymko, V. (2018b). In Pursuit of the Functional Definition of a Mind: the Pivotal Role of a Discourse. Psycholinguistics, 24(1), 403–424. https://doi.org/10.31470/2309-1797-2018-24-1-403-424
Simonov, P.V. (1991). Motivated Brain a Neurophysiological Analysis of Human Behavior. Publisher: Routledge.
Turing, A. (1950). Computing Machinery and Intelligence (pp. 433–460).
Vigliocco, G. (2015). Psychology of Language: From the 20th to the 21st Century. 1st May. Observer. [Online]. [6 February 2019]. Available from https://www.psychologicalscience.org/observer
Vosse, T., & Kempen, G. (2000). Syntactic structure assembly in human parsing: A computational model based on competitive inhibition and lexicalist grammar. Cognition, 75(2), 105–143. https://doi.org/10.1016/S0010-0277(00)00063-9
Wikipedia. (2019). Natural-language understanding. [Online]. [6 February 2019]. Available from https://en.wikipedia.org/wiki/Natural-language_understanding
Yerkes, R.M., & Dodson, J.D. (1908). The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology, 18(5), 459–482. https://doi.org/10.1002/cne.920180503
Abstract views: 214 PDF Downloads: 48 PDF Downloads: 42
This work is licensed under a Creative Commons Attribution 4.0 International License.