Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models

Sweta Karlekar, Tong Niu, Mohit Bansal

Abstract
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.
Anthology ID:
N18-2110
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
701–707
Language:
URL:
https://aclanthology.org/N18-2110
DOI:
10.18653/v1/N18-2110
Bibkey:
Cite (ACL):
Sweta Karlekar, Tong Niu, and Mohit Bansal. 2018. Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 701–707, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Detecting Linguistic Characteristics of Alzheimer’s Dementia by Interpreting Neural Models (Karlekar et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-5/N18-2110.pdf
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