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Sebastian Tur and Maria Saiyak Kaluk Biographical Information
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Domestic Ubimus
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In: EISSN: 2409-9708 ; EAI Endorsed Transactions on Creative Technologies ; https://hal-hprints.archives-ouvertes.fr/hprints-03602695 ; EAI Endorsed Transactions on Creative Technologies, EAI - European Alliance for Innovation, 2022, ⟨10.4108/eai.22-2-2022.173493⟩ (2022)
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Metropolenzeichen: Atlas zur visuellen Mehrsprachigkeit der Metropole Ruhr ...
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Eine Disziplin stellt um: Open-Access-Transformation in der Linguistik ...
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Eine Disziplin stellt um: Open-Access-Transformation in der Linguistik ...
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XTREME-S: Evaluating Cross-lingual Speech Representations ...
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The domestic sources of sub-state foreign policymaking: determinants of subnational development cooperation across European regions ...
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The domestic sources of sub-state foreign policymaking: determinants of subnational development cooperation across European regions ...
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49 - A test-suite for the evaluation of Portuguese-English machine translations ...
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Effects of Spatial Speech Presentation on Listener Response Strategy for Talker-Identification ...
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Detecting Dysfluencies in Stuttering Therapy Using wav2vec 2.0 ...
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Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation ...
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Semantic Feature Extraction Using SBERT for Dementia Detection
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In: Brain Sciences; Volume 12; Issue 2; Pages: 270 (2022)
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Abstract:
Dementia is a neurodegenerative disease that leads to the development of cognitive deficits, such as aphasia, apraxia, and agnosia. It is currently considered one of the most significant major medical problems worldwide, primarily affecting the elderly. This condition gradually impairs the patient’s cognition, eventually leading to the inability to perform everyday tasks without assistance. Since dementia is an incurable disease, early detection plays an important role in delaying its progression. Because of this, tools and methods have been developed to help accurately diagnose patients in their early stages. State-of-the-art methods have shown that the use of syntactic-type linguistic features provides a sensitive and noninvasive tool for detecting dementia in its early stages. However, these methods lack relevant semantic information. In this work, we propose a novel methodology, based on the semantic features approach, by using sentence embeddings computed by Siamese BERT networks (SBERT), along with support vector machine (SVM), K-nearest neighbors (KNN), random forest, and an artificial neural network (ANN) as classifiers. Our methodology extracted 17 features that provide demographic, lexical, syntactic, and semantic information from 550 oral production samples of elderly controls and people with Alzheimer’s disease, provided by the DementiaBank Pitt Corpus database. To quantify the relevance of the extracted features for the dementia classification task, we calculated the mutual information score, which demonstrates a dependence between our features and the MMSE score. The experimental classification performance metrics, such as the accuracy, precision, recall, and F1 score (77, 80, 80, and 80%, respectively), validate that our methodology performs better than syntax-based methods and the BERT approach when only the linguistic features are used.
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Keyword:
dementia; NLP feature extraction; SBERT; semantic analysis; syntax analysis
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URL: https://doi.org/10.3390/brainsci12020270
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Semantic Feature Extraction Using SBERT for Dementia Detection
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In: Brain Sci (2022)
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Scratches? Scribbles? Scripture! Revealing the Unseen : 3D Scanning of Glagolitic Graffiti of the 10th Century at the Monastery of St. Naum
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