  Alzheimer's disease (AD) is an irreversible and progressive brain disease
that can be stopped or slowed down with medical treatment. Language changes
serve as a sign that a patient's cognitive functions have been impacted,
potentially leading to early diagnosis. In this work, we use NLP techniques to
classify and analyze the linguistic characteristics of AD patients using the
DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs,
and their combination, to distinguish between language samples from AD and
control patients. We achieve a new independent benchmark accuracy for the AD
classification task. More importantly, we next interpret what these neural
models have learned about the linguistic characteristics of AD patients, via
analysis based on activation clustering and first-derivative saliency
techniques. We then perform novel automatic pattern discovery inside activation
clusters, and consolidate AD patients' distinctive grammar patterns.
Additionally, we show that first derivative saliency can not only rediscover
previous language patterns of AD patients, but also shed light on the
limitations of neural models. Lastly, we also include analysis of
gender-separated AD data.
