Characterizing Verb Argument Structure in Aphasia Using Dependency Parsers

Verb argument structure (VAS) is often impaired in poststroke aphasia. Previous studies investigated VAS impairments using constrained experiments or less often narrative speech, which relies on time-consuming manual annotations of VAS in speech transcriptions. Here, we aimed to develop and validate an automated approach to quantify VAS use in narrative speech using natural language processing (a dependency parser) and to apply it to a new data set.

First, we validated this approach by comparing manually annotated measures of VAS from a previous study, with automatically annotated VAS measures developed here. We then applied this approach to a new data set of participants with aphasia (n = 106), whose narrative discourse samples were compared to a control group from AphasiaBank to (a) replicate previous findings and (b) assess VAS impairments in agrammatism specifically.

The analysis of the new data set using automatically annotated VAS revealed that participants with Broca's aphasia and, according to grammatical distinctions, agrammatic participants were more likely to select verbs used with fewer arguments, on average, and to produce fewer arguments than controls.

This study reveals that dependency parsers are suitable to characterize VAS use in spontaneous speech with virtually absent manual labor, confirming the feasibility of developing clinically applicable tools for complex language analysis. The results of the study additionally show that participants with aphasia differ from controls in their VAS sensitivity and abilities.