This paper presents a new dataset, B-SHARP, that can be used to develop NLP models for the detection of Mild Cognitive Impairment (MCI) known as an early sign of Alzheimer’s disease. Our dataset contains 1-2 min speech segments from 326 human subjects for 3 topics, (1) daily activity, (2) room environment, and (3) picture description, and their transcripts so that a total of 650 speech segments are collected. Given the B-SHARP dataset, several hierarchical text classification models are developed that jointly learn combinatory features across all 3 topics. The best performance of 74.1% is achieved by an ensemble model that adapts 3 types of transformer encoders. To the best of our knowledge, this is the first work that builds deep learning-based text classification models on multiple contents for the detection of MCI.

This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer’s disease by analyzing their language beyond a syntactic or semantic level. Meta-semantic representation consists of three parts, entities, predicate argument structures, and discourse attributes, that derive rich knowledge graphs. For this study, 50 controls and 50 patients with mild cognitive impairment (MCI) are selected, and meta-semantic representation is annotated on their speeches transcribed in text. Inter-annotator agreement scores of 88%, 82%, and 89% are achieved for the three types of annotation, respectively. Five analyses are made using this annotation, depicting clear distinctions between the control and MCI groups. Finally, a neural model is trained on features extracted from those analyses to classify MCI patients from normal controls, showing a high accuracy of 82% that is very promising.