Multilingual prediction of
| العنوان: | Multilingual prediction of |
|---|---|
| المؤلفون: | Dimitrios Kokkinakis, Nicklas Linz, Philippe Robert, Frank Rudzicz, Jan Alexandersson, Bai Li, Alexandra König, Kathleen C. Fraser, Kristina Lundholm Fors |
| المصدر: | NAACL-HLT (1) |
| بيانات النشر: | Association for Computational Linguistics, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | Domain adaptation, Training set, business.industry, Computer science, computer.software_genre, medicine.disease, Task (project management), 030507 speech-language pathology & audiology, 03 medical and health sciences, 0302 clinical medicine, medicine, Dementia, Language modelling, Language model, Artificial intelligence, 0305 other medical science, business, Set (psychology), computer, 030217 neurology & neurosurgery, Natural language processing |
| الوصف: | 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. |
| الوصول الحر: | https://explore.openaire.eu/search/publication?articleId=doi_________::f36e527a739665c9382b8af3ae241d45Test https://doi.org/10.18653/v1/n19-1367Test |
| رقم الانضمام: | edsair.doi...........f36e527a739665c9382b8af3ae241d45 |
| قاعدة البيانات: | OpenAIRE |
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