Topic coherence analysis for the classification of Alzheimer's disease

Language impairment in Alzheimer’s disease is characterized by a decline in the semantic and pragmatic levels of language processing that manifests since the early stages of the disease. While semantic deficits have been widely investigated using linguistic features, pragmatic deficits are still mostly un-explored. In this work, we present an approach to automatically classify Alzheimer’s disease using a set of pragmatic features extracted from a discourse production task. Following the clinical practice, we consider an image representing a closed domain as a discourse’s elicitation form. Then, we model the elicited speech as a graph that encodes a hierarchy of topics. To do so, the proposed method relies on the integration of various NLP techniques: syntactic parsing for sentence segmentation into clauses, coreference resolution for capturing dependencies among clauses, and word embeddings for identifying semantic relations among topics. According to the experimental results, pragmatic features are able to provide promising results distinguishing individuals with Alzheimer’s disease, comparable to solutions based on other types of linguistic features.