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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]
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Database: Education Research Complete
Description
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]
ISSN:10580360
DOI:10.1044/2016_AJSLP-15-0147