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

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 w...

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Bibliographic Details
Published in:Big data and cognitive computing 2024-01, Vol.8 (1), p.3
Main Authors: Orellana, Marcos, García, Patricio Santiago, Ramon, Guillermo Daniel, Zambrano-Martinez, Jorge Luis, Patiño-León, Andrés, Serrano, María Verónica, Cedillo, Priscila
Format: Article
Language:English
Subjects:
Accuracy
Adults
Algorithms
Artificial intelligence
Augmentative and alternative communication
Autism
Caregivers
Cerebral palsy
Communication
Computational linguistics
Context
Data mining
Language
Language modeling
Language processing
Machine learning
Natural language
Natural language interfaces
Natural language processing
neural network
Older people
Phrases
Pictographs
pre-trained models
Semantic analysis
semantic similarity
Semantics
Similarity
Slang
text mining
word embedding
Word sense disambiguation
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Summary: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.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc8010003