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Relevant bibliographies by topics / Aphasia; Natural language processing

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Journal articles

Academic literature on the topic 'Aphasia; Natural language processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Aphasia; Natural language processing"

1

Ananth Rao, Ananya, and Prof Venkatesh S. "Identification of Aphasia using Natural Language Processing." Journal of University of Shanghai for Science and Technology 23, no. 06 (2021): 1737–47. http://dx.doi.org/10.51201/jusst/21/06488.

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Aphasia is a neurological disorder of language that precludes a person’s ability to speak, understand, read or write in any language. By virtue of this disorder being inextricably connected to language, there is a vast potential for the application of Natural Language Processing (NLP) for the diagnosis of the disorder. This paper surveys the automated machine-learning-based classification methodologies followed by an attempt to discuss a potential way in which an NLP-backed methodology could be implemented along with its accompanying challenges. It is seen that the need for standardized technology-based diagnostic solutions necessitates the exploration of such a methodology.

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2

Schwartz, Myrna F. "Theoretical analysis of word production deficits in adult aphasia." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1634 (2014): 20120390. http://dx.doi.org/10.1098/rstb.2012.0390.

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The cognitive analysis of adult language disorders continues to draw heavily on linguistic theory, but increasingly it reflects the influence of connectionist, spreading activation models of cognition. In the area of spoken word production, ‘localist’ connectionist models represent a natural evolution from the psycholingistic theories of earlier decades. By contrast, the parallel distributed processing framework forces more radical rethinking of aphasic impairments. This paper exemplifies these multiple influences in contemporary cognitive aphasiology. Topics include (i) what aphasia reveals about semantic-phonological interaction in lexical access; (ii) controversies surrounding the interpretation of semantic errors and (iii) a computational account of the relationship between naming and word repetition in aphasia. Several of these topics have been addressed using case series methods, including computational simulation of the individual, quantitative error patterns of diverse groups of patients and analysis of brain lesions that correlate with error rates and patterns. Efforts to map the lesion correlates of nonword errors in naming and repetition highlight the involvement of sensorimotor areas in the brain and suggest the need to better integrate models of word production with models of speech and action.

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3

Zimmerer, Vitor C., and Rosemary A. Varley. "A case of “order insensitivity”? Natural and artificial language processing in a man with primary progressive aphasia." Cortex 69 (August 2015): 212–19. http://dx.doi.org/10.1016/j.cortex.2015.05.006.

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4

Paetzold, Gustavo H., and Lucia Specia. "A Survey on Lexical Simplification." Journal of Artificial Intelligence Research 60 (November 15, 2017): 549–93. http://dx.doi.org/10.1613/jair.5526.

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Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task.

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5

Swinney, D., and E. Zurif. "Syntactic Processing in Aphasia." Brain and Language 50, no. 2 (1995): 225–39. http://dx.doi.org/10.1006/brln.1995.1046.

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6

Jain, Aditya, Gandhar Kulkarni, and Vraj Shah. "Natural Language Processing." International Journal of Computer Sciences and Engineering 6, no. 1 (2018): 161–67. http://dx.doi.org/10.26438/ijcse/v6i1.161167.

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7

Yilmaz, A. Egemen. "Natural Language Processing." International Journal of Systems and Service-Oriented Engineering 4, no. 1 (2014): 68–83. http://dx.doi.org/10.4018/ijssoe.2014010105.

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Requirement analysis is the very first and crucial step in the software development processes. On the other hand, as previously addressed by other researchers, it is the Achilles' heel of the whole process since the requirements lie on the problem space, whereas other software artifacts are on the solution space. Stating the requirements in a clear manner eases the following steps in the process as well as reducing the number of potential errors. In this paper, techniques for the improvement of the requirements expressed in the natural language are revisited. These techniques try to check the requirement quality attributes via lexical and syntactic analysis methods sometimes with generic, and sometimes domain and application specific knowledge bases.

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8

HASHIDA, KOICHI. "Natural Language Processing." Journal of the Institute of Electrical Engineers of Japan 121, no. 3 (2001): 195–98. http://dx.doi.org/10.1541/ieejjournal.121.195.

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9

N. O. Sadiku, Matthew, Yu Zhou, and Sarhan M. Musa. "Natural Language Processing." International Journal of Advances in Scientific Research and Engineering 4, no. 5 (2018): 68–70. http://dx.doi.org/10.31695/ijasre.2018.32708.

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10

Covington, Michael A., Fernando C. N. Pereira, and Barbara J. Grosz. "Natural Language Processing." Language 71, no. 3 (1995): 652. http://dx.doi.org/10.2307/416262.

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