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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Big Data and Cognitive Computing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-2289/8/1/3 |
| _version_ | 1797344617465643008 |
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| author | Marcos Orellana Patricio Santiago García Guillermo Daniel Ramon Jorge Luis Zambrano-Martinez Andrés Patiño-León María Verónica Serrano Priscila Cedillo |
| author_facet | Marcos Orellana Patricio Santiago García Guillermo Daniel Ramon Jorge Luis Zambrano-Martinez Andrés Patiño-León María Verónica Serrano Priscila Cedillo |
| author_sort | Marcos Orellana |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T11:05:19Z |
| format | Article |
| id | doaj.art-6dda3a7511354bada56bf6744e357a8c |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-08T11:05:19Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-6dda3a7511354bada56bf6744e357a8c2024-01-26T15:05:29ZengMDPI AGBig Data and Cognitive Computing2504-22892023-12-0181310.3390/bdcc8010003Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained ModelMarcos Orellana0Patricio Santiago García1Guillermo Daniel Ramon2Jorge Luis Zambrano-Martinez3Andrés Patiño-León4María Verónica Serrano5Priscila Cedillo6Computer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Research and Development Laboratory (LIDI), Universidad del Azuay, Cuenca 010204, EcuadorComputer Science Department, Universidad de Cuenca, Cuenca 010203, EcuadorHealth 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.https://www.mdpi.com/2504-2289/8/1/3semantic similaritypre-trained modelsnatural language processingtext miningneural networkword embedding |
| spellingShingle | Marcos Orellana Patricio Santiago García Guillermo Daniel Ramon Jorge Luis Zambrano-Martinez Andrés Patiño-León María Verónica Serrano Priscila Cedillo Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model Big Data and Cognitive Computing semantic similarity pre-trained models natural language processing text mining neural network word embedding |
| title | Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model |
| title_full | Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model |
| title_fullStr | Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model |
| title_full_unstemmed | Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model |
| title_short | Semantic Similarity of Common Verbal Expressions in Older Adults through a Pre-Trained Model |
| title_sort | semantic similarity of common verbal expressions in older adults through a pre trained model |
| topic | semantic similarity pre-trained models natural language processing text mining neural network word embedding |
| url | https://www.mdpi.com/2504-2289/8/1/3 |
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