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

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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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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11

Rindflesch, Thomas C. "Natural Language Processing." Annual Review of Applied Linguistics 16 (March 1996): 70–85. http://dx.doi.org/10.1017/s0267190500001446.

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Work in computational linguistics began very soon after the development of the first computers (Booth, Brandwood and Cleave 1958), yet in the intervening four decades there has been a pervasive feeling that progress in computer understanding of natural language has not been commensurate with progress in other computer applications. Recently, a number of prominent researchers in natural language processing met to assess the state of the discipline and discuss future directions (Bates and Weischedel 1993). The consensus of this meeting was that increased attention to large amounts of lexical and domain knowledge was essential for significant progress, and current research efforts in the field reflect this point of view.

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12

Wilks, Yorick. "Natural language processing." Communications of the ACM 39, no. 1 (1996): 60–62. http://dx.doi.org/10.1145/234173.234180.

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13

Heidorn, P. Bryan. "Natural language processing." Information Processing & Management 32, no. 1 (1996): 122–23. http://dx.doi.org/10.1016/s0306-4573(96)90089-8.

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14

JOSHI, A. K. "Natural Language Processing." Science 253, no. 5025 (1991): 1242–49. http://dx.doi.org/10.1126/science.253.5025.1242.

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15

Patten, T., and P. Jacobs. "Natural-language processing." IEEE Expert 9, no. 1 (1994): 35. http://dx.doi.org/10.1109/64.295134.

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16

Weischedel, R., J. Carbonell, B. Grosz, et al. "Natural Language Processing." Annual Review of Computer Science 4, no. 1 (1990): 435–52. http://dx.doi.org/10.1146/annurev.cs.04.060190.002251.

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17

Hirschberg, Julia, Bruce W. Ballard, and Donald Hindle. "Natural Language Processing." AT&T Technical Journal 67, no. 1 (1988): 41–57. http://dx.doi.org/10.1002/j.1538-7305.1988.tb00232.x.

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18

Chowdhury, Gobinda G. "Natural language processing." Annual Review of Information Science and Technology 37, no. 1 (2005): 51–89. http://dx.doi.org/10.1002/aris.1440370103.

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19

Martinez, Angel R. "Natural language processing." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 3 (2010): 352–57. http://dx.doi.org/10.1002/wics.76.

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20

Horacek, Helmut. "Natural language processing." Computer Physics Communications 61, no. 1-2 (1990): 76–92. http://dx.doi.org/10.1016/0010-4655(90)90107-c.

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21

Grosz, Barbara J. "Natural-language processing." Artificial Intelligence 25, no. 1 (1985): 1–4. http://dx.doi.org/10.1016/0004-3702(85)90038-4.

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22

Selfridge, Mallory. "Natural language processing." Artificial Intelligence in Engineering 2, no. 1 (1987): 50. http://dx.doi.org/10.1016/0954-1810(87)90076-8.

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23

Niimi, Akio. "Natural Language Processing." Chest 159, no. 6 (2021): 2149–50. http://dx.doi.org/10.1016/j.chest.2021.01.045.

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24

Marcus, M. "New trends in natural language processing: statistical natural language processing." Proceedings of the National Academy of Sciences 92, no. 22 (1995): 10052–59. http://dx.doi.org/10.1073/pnas.92.22.10052.

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25

Milberg, William. "Information processing deficits and aphasia." Aphasiology 2, no. 3-4 (1988): 359–62. http://dx.doi.org/10.1080/02687038808248938.

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26

Geigenberger, Andrea, and Wolfram Ziegler. "Receptive prosodic processing in aphasia." Aphasiology 15, no. 12 (2001): 1169–87. http://dx.doi.org/10.1080/02687040143000555.

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27

Grefenstette, Gregory, and Frédérique Segond. "Multilingual Natural Language Processing." International Journal of Corpus Linguistics 2, no. 1 (1997): 153–62. http://dx.doi.org/10.1075/ijcl.2.1.08gre.

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28

Kim, Jin-Dong. "Biomedical Natural Language Processing." Computational Linguistics 43, no. 1 (2017): 265–67. http://dx.doi.org/10.1162/coli_r_00281.

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29

Salt, Jessica, Polina Harik, and Michael A. Barone. "Leveraging Natural Language Processing." Academic Medicine 94, no. 3 (2019): 314–16. http://dx.doi.org/10.1097/acm.0000000000002558.

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30

Farghaly, Ali, and Khaled Shaalan. "Arabic Natural Language Processing." ACM Transactions on Asian Language Information Processing 8, no. 4 (2009): 1–22. http://dx.doi.org/10.1145/1644879.1644881.

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31

Søgaard, Anders. "Explainable Natural Language Processing." Synthesis Lectures on Human Language Technologies 14, no. 3 (2021): 1–123. http://dx.doi.org/10.2200/s01118ed1v01y202107hlt051.

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32

Thompson, Cynthia K. "Neurocognitive Recovery of Sentence Processing in Aphasia." Journal of Speech, Language, and Hearing Research 62, no. 11 (2019): 3947–72. http://dx.doi.org/10.1044/2019_jslhr-l-rsnp-19-0219.

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Purpose Reorganization of language networks in aphasia takes advantage of the facts that (a) the brain is an organ of plasticity, with neuronal changes occurring throughout the life span, including following brain damage; (b) plasticity is highly experience dependent; and (c) as with any learning system, language reorganization involves a synergistic interplay between organism-intrinsic (i.e., cognitive and brain) and organism-extrinsic (i.e., environmental) variables. A major goal for clinical treatment of aphasia is to be able to prescribe treatment and predict its outcome based on the neurocognitive deficit profiles of individual patients. This review article summarizes the results of research examining the neurocognitive effects of psycholinguistically based treatment (i.e., Treatment of Underlying Forms; Thompson & Shapiro, 2005 ) for sentence processing impairments in individuals with chronic agrammatic aphasia resulting from stroke and primary progressive aphasia and addresses both behavioral and brain variables related to successful treatment outcomes. The influences of lesion volume and location, perfusion (blood flow), and resting-state neural activity on language recovery are also discussed as related to recovery of agrammatism and other language impairments. Based on these and other data, principles for promoting neuroplasticity of language networks are presented. Conclusions Sentence processing treatment results in improved comprehension and production of complex syntactic structures in chronic agrammatism and generalization to less complex, linguistically related structures in chronic agrammatism. Patients also show treatment-induced shifts toward normal-like online sentence processing routines (based on eye movement data) and changes in neural recruitment patterns (based on functional neuroimaging), with posttreatment activation of regions overlapping with those within sentence processing and dorsal attention networks engaged by neurotypical adults performing the same task. These findings provide compelling evidence that treatment focused on principles of neuroplasticity promotes neurocognitive recovery in chronic agrammatic aphasia. Presentation Video https://doi.org/10.23641/asha.10257587

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33

Wilson, Stephen M., Dana K. Eriksson, Melodie Yen, Andrew T. Demarco, Sarah M. Schneck, and Jillian M. Lucanie. "Language Mapping in Aphasia." Journal of Speech, Language, and Hearing Research 62, no. 11 (2019): 3937–46. http://dx.doi.org/10.1044/2019_jslhr-l-rsnp-19-0031.

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Purpose Recovery from aphasia is thought to depend on neural plasticity, that is, functional reorganization of surviving brain regions such that they take on new or expanded roles in language processing. To make progress in characterizing the nature of this process, we need feasible, reliable, and valid methods for identifying language regions of the brain in individuals with aphasia. This article reviews 3 recent studies from our lab in which we have developed and validated several novel functional magnetic resonance imaging paradigms for language mapping in aphasia. Method In the 1st study, we investigated the reliability and validity of 4 language mapping paradigms in neurologically normal older adults. In the 2nd study, we developed a novel adaptive semantic matching paradigm and assessed its feasibility, reliability, and validity in individuals with and without aphasia. In the 3rd study, we developed and evaluated 2 additional adaptive paradigms—rhyme judgment and syllable counting—for mapping phonological encoding regions. Results We found that the adaptive semantic matching paradigm could be performed by most individuals with aphasia and yielded reliable and valid maps of core perisylvian language regions in each individual participant. The psychometric properties of this paradigm were superior to those of other commonly used paradigms such as narrative comprehension and picture naming. The adaptive rhyme judgment paradigm was capable of identifying fronto-parietal phonological encoding regions in individual participants. Conclusion Adaptive language mapping paradigms offer a promising approach for future research on the neural basis of recovery from aphasia. Presentation Video https://doi.org/10.23641/asha.10257584

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34

Milberg, W., S. E. Blumstein, and B. Dworetzky. "Processing of lexical ambiguities in aphasia." Brain and Language 31, no. 1 (1987): 138–50. http://dx.doi.org/10.1016/0093-934x(87)90065-4.

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35

Timimi, Farris, Sara Ray, Erik Jones, Lee Aase, and Kathleen Hoffman. "Patient-Reported Outcomes in Online Communications on Statins, Memory, and Cognition: Qualitative Analysis Using Online Communities." Journal of Medical Internet Research 21, no. 11 (2019): e14809. http://dx.doi.org/10.2196/14809.

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Background In drug development clinical trials, there is a need for balance between restricting variables by setting eligibility criteria and representing the broader patient population that may use a product once it is approved. Similarly, although recent policy initiatives focusing on the inclusion of historically underrepresented groups are being implemented, barriers still remain. These limitations of clinical trials may mask potential product benefits and side effects. To bridge these gaps, online communication in health communities may serve as an additional population signal for drug side effects. Objective The aim of this study was to employ a nontraditional dataset to identify drug side-effect signals. The study was designed to apply both natural language processing (NLP) technology and hands-on linguistic analysis to a set of online posts from known statin users to (1) identify any underlying crossover between the use of statins and impairment of memory or cognition and (2) obtain patient lexicon in their descriptions of experiences with statin medications and memory changes. Methods Researchers utilized user-generated content on Inspire, looking at over 11 million posts across Inspire. Posts were written by patients and caregivers belonging to a variety of communities on Inspire. After identifying these posts, researchers used NLP and hands-on linguistic analysis to draw and expand upon correlations among statin use, memory, and cognition. Results NLP analysis of posts identified statistical correlations between statin users and the discussion of memory impairment, which were not observed in control groups. NLP found that, out of all members on Inspire, 3.1% had posted about memory or cognition. In a control group of those who had posted about TNF inhibitors, 6.2% had also posted about memory and cognition. In comparison, of all those who had posted about a statin medication, 22.6% (P<.001) also posted about memory and cognition. Furthermore, linguistic analysis of a sample of posts provided themes and context to these statistical findings. By looking at posts from statin users about memory, four key themes were found and described in detail in the data: memory loss, aphasia, cognitive impairment, and emotional change. Conclusions Correlations from this study point to a need for further research on the impact of statins on memory and cognition. Furthermore, when using nontraditional datasets, such as online communities, NLP and linguistic methodologies broaden the population for identifying side-effect signals. For side effects such as those on memory and cognition, where self-reporting may be unreliable, these methods can provide another avenue to inform patients, providers, and the Food and Drug Administration.

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36

Reilly, Jamie. "Semantic Memory and Language Processing in Aphasia and Dementia." Seminars in Speech and Language 29, no. 1 (2008): 003–4. http://dx.doi.org/10.1055/s-2008-1061620.

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37

Holland, Audrey. "Semantic Memory and Language Processing in Aphasia and Dementia." Seminars in Speech and Language 29, no. 1 (2008): 001. http://dx.doi.org/10.1055/s-2008-1062600.

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38

Neto, Bruna, and Maria Emília Santos. "Language after aphasia: Only a matter of speed processing?" Aphasiology 26, no. 11 (2012): 1352–61. http://dx.doi.org/10.1080/02687038.2012.672023.

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39

Menn, Lise, and Roelien Bastiaanse. "Beyond Chomsky versus Skinner: frequency, language processing and aphasia." Aphasiology 30, no. 11 (2016): 1169–73. http://dx.doi.org/10.1080/02687038.2016.1168920.

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40

Minhwa Chung and D. Moldevan. "Applying parallel processing to natural-language processing." IEEE Expert 9, no. 1 (1994): 36–44. http://dx.doi.org/10.1109/64.295133.

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41

Wright, Heather Harris, and Rebecca J. Shisler. "Working Memory in Aphasia." American Journal of Speech-Language Pathology 14, no. 2 (2005): 107–18. http://dx.doi.org/10.1044/1058-0360(2005/012).

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Recently, researchers have suggested that deficits in working memory capacity contribute to language-processing difficulties observed in individuals with aphasia (e.g., I. Caspari, S. Parkinson, L. LaPointe, & R. Katz, 1998; R. A. Downey et al., 2004; N. Friedmann & A. Gvion, 2003; H. H. Wright, M. Newhoff, R. Downey, & S. Austermann, 2003). A theoretical framework of working memory can aid in our understanding of a disrupted system (e.g., after stroke) and how this relates to language comprehension and production. Additionally, understanding the theoretical basis of working memory is important for the measurement and treatment of working memory. The literature indicates that future investigations of measurement and treatment of working memory are warranted in order to determine the role of working memory in language processing.

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42

Pavičić Dokoza, Katarina, and Zdravko Kolundžić. "Auditory Processing in People with Chronic Aphasia." Collegium antropologicum 44, no. 2 (2020): 95–102. http://dx.doi.org/10.5671/ca.44.2.5.

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The preconditions for successful voice communication are good hearing and listening, and auditory processing that includes the ability to process the audio signal. Damage or deceleration of sound signal processing at any level from the periphery to the central structures leads to disintegration and inability to process the signal effectively. Auditory processing in people with aphasia has not been examined in Croatia to date. Results of studies conducted in other languages point to negative effects of auditory processing difficulties on the receptive and expressive component of the language. This study was conducted on a sample of subjects with chronic aphasia and a group of control subjects with no neurological or any other disorders that can affect auditory processing. The inclusion criteria for persons with aphasia were impaired language skills as a result of cerebrovascular accident that occurred at least six months before the examination, regardless of severity and type of aphasia and normal hearing status. The study did not include persons with aphasia who were unable to repeat the six-word sentence, due to impaired comprehension or speech expression, and those whose comprehension was not sufficient to cooperate well during the test. The test was conducted individually for 30 minutes per subject using the Auditory processing test (PSP) that is standardized for the Croatian language. Results from this study showed statistically significant lower achievement on all subtests on PSP-1 (filtered words, speech in noise, dichotic words test, and dichotic sentence test) in people with aphasia compared with the control group. People with aphasia and control group subjects showed better results in favor of the left ear on variable speech in noise. Filtered words were easily processed through the left ear in people with aphasia while dichotic sentences were easily processed through the left ear in the control group. The results of this study confirm the hypothesis of the presence of auditory processing difficulties in people with aphasia and are consistent with previous studies conducted in other languages. In addition, the study points to the need of introducing specific therapeutic procedures in rehabilitation in order to improve the function of auditory processing in persons after a cerebrovascular accident.

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43

Swinney, David, Edgar Zurif, Penny Prather, and Tracy Love. "Neurological Distribution of Processing Resources Underlying Language Comprehension." Journal of Cognitive Neuroscience 8, no. 2 (1996): 174–84. http://dx.doi.org/10.1162/jocn.1996.8.2.174.

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Using a cross-modal lexical priming technique we provide an on-line examination of the ability of aphasic patients to construct syntactically licensed dependencies in real time. We show a distinct difference between Wernicke's and Broca's aphasic patients with respect to this form of syntactic processing: the Wernicke's patients link the elements of dependency relations in the same manner as do neurologically intact individuals; the Broca's patients show no evidence of such linkage. These findings indicate that the cerebral tissue implicated in Wernicke's aphasia is not crucial for recovering syntactically licensed structural dependencies, while that implicated in Broca's aphasia is. Moreover, additional considerations suggest that the latter region is not the locus of syntactic representations per se, but rather provides the resources that sustain the normal operating characteristics of the lexical processing system—characteristics that are, in turn, necessary for building syntactic representations in real time.

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44

Patil, Gouri Shanker, R. Rangasayee, and Geetha Mukundan. "Non-fluent aphasia in deaf user of Indian Sign Language." Cognitive Linguistic Studies 1, no. 1 (2014): 147–53. http://dx.doi.org/10.1075/cogls.1.1.07pat.

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The current study describes aphasia in a deaf user of Indian Sign Language (ISL). One congenitally deaf adult with LHD was evaluated for signs of aphasia. The tools used were Aphasia Diagnostic Battery in Indian Sign Language (ADB in ISL), Magnetic Resonance Imaging (MRI) investigation, linguistic, and neurobehavioral profile. The results of all investigative procedures revealed signs and symptoms consistent with non-fluent aphasia specifically Broca’s aphasia. The data from ISL in brain damaged individual further emphasize the role of left hemisphere in sign language processing.

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45

Radev, Dragomir R., and Rada Mihalcea. "Networks and Natural Language Processing." AI Magazine 29, no. 3 (2008): 16. http://dx.doi.org/10.1609/aimag.v29i3.2160.

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Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word-sense disambiguation, ontology construction, sentiment and subjectivity analysis, and text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in language processing and try to explain how and why they work.

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46

Goyal, Shipra. "NATURAL LANGUAGE PROCESSING ITS TYPES." International Journal of Advanced Research in Computer Science 8, no. 8 (2017): 260–63. http://dx.doi.org/10.26483/ijarcs.v8i8.4362.

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47

Sadiku, Matthew N. O., Yu Zhou, and Sarhan M. Musa. "NATURAL LANGUAGE PROCESSING IN HEALTHCARE." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 5 (2018): 39. http://dx.doi.org/10.23956/ijarcsse.v8i5.626.

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Natural language processing (NLP) refers to the process of using of computer algorithms to identify key elements in everyday language and extract meaning from unstructured spoken or written communication. Healthcare is the biggest user of the NLP tools. It is expected that NLP tools should be able to bridge the gap between the mountain of data generated daily and the limited cognitive capacity of the human mind. This paper provides a brief introduction on the use of NLP in healthcare.

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48

Covington, Michael A., Madeleine Bates, and Ralph M. Weischedel. "Challenges in Natural Language Processing." Language 71, no. 2 (1995): 402. http://dx.doi.org/10.2307/416182.

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49

Barnett, Jim, Kevin Knight, Inderjeet Mani, and Elaine Rich. "Knowledge and natural language processing." Communications of the ACM 33, no. 8 (1990): 50–71. http://dx.doi.org/10.1145/79173.79177.

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50

King, Margaret. "Evaluating natural language processing systems." Communications of the ACM 39, no. 1 (1996): 73–79. http://dx.doi.org/10.1145/234173.234208.

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