Investigating the Medical Coverage of a Translation System into Pictographs for Patients with an Intellectual Disability

Norré, Magali; Vandeghinste, Vincent; Thomas, François; Bouillon, Pierrette

doi:10.18653/v1/2022.slpat-1.6

Cited by 1 publication

(1 citation statement)

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“…Another work involving NLP and pictograms is presented by Norré et al [9], through the Word Sense Disambiguation (WSD) task that automatically translates French dialogues between doctors and patients with intellectual disabilities into ARASAAC pictogram sequences. For this purpose, they evaluate different pre-trained language models and Word Embeddings models.…”

Section: Related Workmentioning

confidence: 99%

Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model

Orellana,

García,

Ramon

et al. 2023

BDCC

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Health problems in older adults lead to situations where communication with peers, family and caregivers becomes challenging for seniors; therefore, it is necessary to use alternative methods to facilitate communication. In this context, Augmentative and Alternative Communication (AAC) methods are widely used to support this population segment. Moreover, with Artificial Intelligence (AI), and specifically, machine learning algorithms, AAC can be improved. Although there have been several studies in this field, it is interesting to analyze common phrases used by seniors, depending on their context (i.e., slang and everyday expressions typical of their age). This paper proposes a semantic analysis of the common phrases of older adults and their corresponding meanings through Natural Language Processing (NLP) techniques and a pre-trained language model using semantic textual similarity to represent the older adults’ phrases with their corresponding graphic images (pictograms). The results show good scores achieved in the semantic similarity between the phrases of the older adults and the definitions, so the relationship between the phrase and the pictogram has a high degree of probability.

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Section: Related Workmentioning

confidence: 99%