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Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling

Conference paper
Authors Kathleen Fraser
Nicklas Linz
Kristina Lundholm Fors
Frank Rudzicz
Alexandra König
Jan Alexandersson
Philippe Robert
Dimitrios Kokkinakis
Published in Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics.
Place of publication Minneapolis, Minnesota. United States.
Publication year 2019
Published at Department of Swedish
Language en
Links https://www.aclweb.org/anthology/N1...
Keywords domain adaptation, dementia
Subject categories Language Technology (Computational Linguistics), Computational linguistics

Abstract

There is growing evidence that changes in speech and language may be early markers of dementia, but much of the previous NLP work in this area has been limited by the size of the available datasets. Here, we compare several methods of domain adaptation to augment a small French dataset of picture descriptions (n = 57) with a much larger English dataset (n = 550), for the task of automatically distinguishing participants with dementia from controls. The first challenge is to identify a set of features that transfer across languages; in addition to previously used features based on information units, we introduce a new set of features to model the order in which information units are produced by dementia patients and controls. These concept-based language model features improve classification performance in both English and French separately, and the best result (AUC = 0.89) is achieved using the multilingual training set with a combination of information and language model features.

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