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Multilingual word embeddings for the assessment of narrative speech in mild cognitive impairment

Artikel i vetenskaplig tidskrift
Författare Kathleen Fraser
Kristina Lundholm Fors
Dimitrios Kokkinakis
Publicerad i Computer Speech and Language
Volym 53
Sidor 121-139
ISSN 0885-2308
Publiceringsår 2019
Publicerad vid Institutionen för svenska språket
Sidor 121-139
Språk en
Länkar doi.org/10.1016/j.csl.2018.07.005
https://gup.ub.gu.se/file/207540
Ämnesord Dementia, Machine learning, Mild cognitive impairment, Multilingual analysis, Narrative analysis, Topic modeling
Ämneskategorier Språkteknologi (språkvetenskaplig databehandling)

Sammanfattning

© 2018 The Authors We analyze the information content of narrative speech samples from individuals with mild cognitive impairment (MCI), in both English and Swedish, using a combination of supervised and unsupervised learning techniques. We extract information units using topic models trained on word embeddings in monolingual and multilingual spaces, and find that the multilingual approach leads to significantly better classification accuracies than training on the target language alone. In many cases, we find that augmenting the topic model training corpus with additional clinical data from a different language is more effective than training on additional monolingual data from healthy controls. Ultimately we are able to distinguish MCI speakers from healthy older adults with accuracies of up to 63% (English) and 72% (Swedish) on the basis of information content alone. We also compare our method against previous results measuring information content in Alzheimer's disease, and report an improvement over other topic-modeling approaches. Furthermore, our results support the hypothesis that subtle differences in language can be detected in narrative speech, even at the very early stages of cognitive decline, when scores on screening tools such as the Mini-Mental State Exam are still in the “normal” range.

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