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Algorithmic Classification of Five Characteristic Types of Paraphasias.

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Title: Algorithmic Classification of Five Characteristic Types of Paraphasias.
Authors: Fergadiotis, Gerasimos1 gfergadiotis@pdx.edu, Gorman, Kyle2, Bedrick, Steven2
Source: American Journal of Speech-Language Pathology. 2016 SpecialIssue, Vol. 25, pS776-S787. 12p. 4 Charts, 2 Graphs.
Subject Terms: *Errors, *New words, *Phonological awareness, *Algorithms, *Aphasia, Paragrammatism, Algorithm research, Automatic classification, Anomia, Onomasiology, Aphasic persons, Linguistics, Phonetics, Semantics, Receiver operating characteristic curves
Geographic Terms: United States
Abstract: Purpose: This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). Method: We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Results: Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Conclusion: Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes. [ABSTRACT FROM AUTHOR]
Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: <searchLink fieldCode="JN" term="%22American+Journal+of+Speech-Language+Pathology%22">American Journal of Speech-Language Pathology</searchLink>. 2016 SpecialIssue, Vol. 25, pS776-S787. 12p. 4 Charts, 2 Graphs.
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  Data: Purpose: This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). Method: We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Results: Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Conclusion: Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of American Journal of Speech-Language Pathology is the property of American Speech-Language-Hearing Association and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1044/2016_AJSLP-15-0147
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        Text: English
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        StartPage: S776
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      – SubjectFull: Phonological awareness
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      – SubjectFull: Algorithms
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      – SubjectFull: Aphasia
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      – SubjectFull: Linguistics
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      – SubjectFull: Phonetics
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      – SubjectFull: Receiver operating characteristic curves
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      – SubjectFull: United States
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      – TitleFull: Algorithmic Classification of Five Characteristic Types of Paraphasias.
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