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Exploring neural models for predicting dementia from language

University of British Columbia

In this thesis we explore the effectiveness of neural models that require no task-specific feature for automatic dementia prediction. The problem is about classifying Alzheimer's disease (AD) from recordings of patients undergoing the Boston Diagnostic Aphasia Examination (BDAE). First we use a multimodal neural model to fuse linguistic features and acoustic features, and investigate the performance change compared to simply concatenating these features. Then we propose a novel coherence feature generated by a neural coherence model, and evaluate the predictiveness of this new feature for dementia prediction. Finally we apply an end-to-end neural method which is free from feature engineering and achieves state-of-the-art classification result on a widely used dementia dataset. We further interpret the predictions made by this neural model from different angles, including model visualization and statistical tests.

Text

2019-08-09

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/71274

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/