Abstract
Agrammatic aphasia is a serious language impairment which can occur after a stroke or traumatic brain injury. We present an automatic method for analyzing aphasic speech using surface level parse features and context-free grammar production rules. Examining these features individually, we show that we can uncover many of the same characteristics of agrammatic language that have been reported in studies using manual analysis. When taken together, these parse features can be used to train a classifier to accurately predict whether or not an individual has aphasia. Furthermore, we find that the parse features can lead to higher classification accuracies than traditional measures of syntactic complexity. Finally, we find that a minimal amount of pre-processing can lead to better results than using either the raw data or highly processed data.
Original language | English (US) |
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Title of host publication | ACL 2014 - BioNLP 2014, Workshop on Biomedical Natural Language Processing, Proceedings of the Workshop |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 134-142 |
Number of pages | 9 |
ISBN (Electronic) | 9781941643181 |
State | Published - 2014 |
Event | ACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014 - Baltimore, United States Duration: Jun 27 2014 → Jun 28 2014 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Conference
Conference | ACL 2014 Workshop on Biomedical Natural Language Processing, BioNLP 2014 |
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Country/Territory | United States |
City | Baltimore |
Period | 6/27/14 → 6/28/14 |
Funding
This research was supported by the Natural Sciences and Engineering Research Council of Canada and National Institutes of Health R01DC01948 and R01DC008552.
ASJC Scopus subject areas
- Computer Science Applications
- Linguistics and Language
- Language and Linguistics