@inproceedings{fraser-etal-2019-multilingual,
title = "Multilingual prediction of {A}lzheimer{'}s disease through domain adaptation and concept-based language modelling",
author = {Fraser, Kathleen C. and
Linz, Nicklas and
Li, Bai and
Lundholm Fors, Kristina and
Rudzicz, Frank and
K{\"o}nig, Alexandra and
Alexandersson, Jan and
Robert, Philippe and
Kokkinakis, Dimitrios},
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1367",
doi = "10.18653/v1/N19-1367",
pages = "3659--3670",
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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<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.</abstract>
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%0 Conference Proceedings
%T Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling
%A Fraser, Kathleen C.
%A Linz, Nicklas
%A Li, Bai
%A Lundholm Fors, Kristina
%A Rudzicz, Frank
%A König, Alexandra
%A Alexandersson, Jan
%A Robert, Philippe
%A Kokkinakis, Dimitrios
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F fraser-etal-2019-multilingual
%X 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.
%R 10.18653/v1/N19-1367
%U https://aclanthology.org/N19-1367
%U https://doi.org/10.18653/v1/N19-1367
%P 3659-3670
Markdown (Informal)
[Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling](https://aclanthology.org/N19-1367) (Fraser et al., NAACL 2019)
ACL
- Kathleen C. Fraser, Nicklas Linz, Bai Li, Kristina Lundholm Fors, Frank Rudzicz, Alexandra König, Jan Alexandersson, Philippe Robert, and Dimitrios Kokkinakis. 2019. Multilingual prediction of Alzheimer’s disease through domain adaptation and concept-based language modelling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3659–3670, Minneapolis, Minnesota. Association for Computational Linguistics.