This article describes the design and implementation of a prototype that analyzes and classifies transcripts of interviews collected during an experiment that involved lateral-brain damage patients. The patients' utterances are classified as instances of categorization, prediction and explanation (abduction) based on surface linguistic cues. The agreement between our automatic classifier and human annotators is measured. The agreement is statistically significant, thus showing that the classification can be performed in an automatic fashion.
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