Using statistical parsing to detect agrammatic aphasia
Kathleen Fraser, Graeme Hirst, Jed A. Meltzer, Jennifer E. Mack, Cynthia K. Thompson · 2014
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.