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)

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

المؤلفون: Hali Lindsay, Alexandra König, Nicklas Linz, Kathleen C. Fraser

المساهمون: National Research Council of Canada (NRC), Deutsches Forschungszentrum für Künstliche Intelligenz GmbH = German Research Center for Artificial Intelligence (DFKI), Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)

المصدر: Computational Linguistics and Clinical Psychology : From Keyboard to Clinic
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology
CLPsyc 2019-Sixth Workshop on Computational Linguistics and Clinical Psychology, Jun 2019, Minneapolis, Minnesota, United States. pp.55-61, ⟨10.18653/v1/W19-3007⟩

مصطلحات موضوعية: Data sharing, [SCCO]Cognitive science, Annotation, Research groups, Computer science, Position paper, Transcription (software), Data science

الوصف: Increased access to large datasets has driven progress in NLP. However, most computational studies of clinically-validated, patient-generated speech and language involve very few datapoints, as such data are difficult (and expensive) to collect. In this position paper, we argue that we must find ways to promote data sharing across research groups, in order to build datasets of a more appropriate size for NLP and machine learning analysis. We review the benefits and challenges of sharing clinical language data, and suggest several concrete actions by both clinical and NLP researchers to encourage multi-site and multi-disciplinary data sharing. We also propose the creation of a collaborative data sharing platform, to allow NLP researchers to take a more active responsibility for data transcription, annotation, and curation.
Sixth Workshop on Computational Linguistics and Clinical Psychology, June 6, 2019, Minneapolis, Minnesota, United States

وصف الملف: application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1eccc014d914a939893fc6cb7d053fc4Test
https://doi.org/10.18653/v1/w19-3007Test

المؤلفون: Marie Eckerström, Jan Alexandersson, Kristina Lundholm Fors, Nicklas Linz, Dimitrios Kokkinakis, Hali Lindsay

المصدر: Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology.

مصطلحات موضوعية: Vocabulary, medicine.medical_specialty, medicine.diagnostic_test, media_common.quotation_subject, Word count, Neuropsychology, Audiology, Task (project management), Interval (music), medicine, Verbal fluency test, Neuropsychological assessment, Psychology, Word (group theory), media_common

الوصف: The Semantic Verbal Fluency (SVF) task is a classical neuropsychological assessment where persons are asked to produce words belonging to a semantic category (e.g., animals) in a given time. This paper introduces a novel method of temporal analysis for SVF tasks utilizing time intervals and applies it to a corpus of elderly Swedish subjects (mild cognitive impairment, subjective cognitive impairment and healthy controls). A general decline in word count and lexical frequency over the course of the task is revealed, as well as an increase in word transition times. Persons with subjective cognitive impairment had a higher word count during the last intervals, but produced words of the same lexical frequencies. Persons with MCI had a steeper decline in both word count and lexical frequencies during the third interval. Additional correlations with neuropsychological scores suggest these findings are linked to a person’s overall vocabulary size and processing speed, respectively. Classification results improved when adding the novel features (AUC=0.72), supporting their diagnostic value.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_________::0c8dbc3f3e0001bd0c156473ff5c3548Test
https://doi.org/10.18653/v1/w19-3012Test