A comparison of syntax, semantics, and pragmatics in spoken language among residents with Alzheimer's disease in managed-care facilities

This research is a discriminative analysis of conversational dialogues involving individuals suffering from dementia of Alzheimer's type. Several metric analyses are applied to the transcripts of the Carolina Conversation Corpus in order to determine if there are significant statistical differences between individuals with and without Alzheimer's disease. Our prior research suggests that there exist measurable linguistic differences between managed-care residents diagnosed with Alzheimer's disease and their caregivers. This paper presents results comparing managed-care residents diagnosed with Alzheimer's disease to other managed-care residents. Results from the analysis indicate that part-of-speech and lexical richness statistics may not be good distinguishing attributes. However, go-ahead utterances and certain fluency measures provide defensible means of differentiating the linguistic characteristics of spontaneous speech between individuals that are and are not diagnosed with Alzheimer's disease. Two machine learning algorithms were able to classify the speech of individuals with and without dementia of the Alzheimer's type with accuracy up to 80%.