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Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information
We propose a deep graph learning approach for computing semantic textual similarity (STS) by using semantic role labels generated by a Semantic Role Labeling (SRL) system. SRL system output has significant challenges in dealing with graph-neural networks because it doesn't have a graph structur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428166/ https://www.ncbi.nlm.nih.gov/pubmed/36042274 http://dx.doi.org/10.1038/s41598-022-19259-5 |
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author | Mohebbi, Majid Razavi, Seyed Naser Balafar, Mohammad Ali |
author_facet | Mohebbi, Majid Razavi, Seyed Naser Balafar, Mohammad Ali |
author_sort | Mohebbi, Majid |
collection | PubMed |
description | We propose a deep graph learning approach for computing semantic textual similarity (STS) by using semantic role labels generated by a Semantic Role Labeling (SRL) system. SRL system output has significant challenges in dealing with graph-neural networks because it doesn't have a graph structure. To address these challenges, we propose a novel SRL graph by using semantic role labels and dependency grammar. For processing the SRL graph, we proposed a Deep Graph Neural Network (DGNN) based graph-U-Net model that is placed on top of the transformers to use a variety of transformers to process representations obtained from them. We investigate the effect of using the proposed DGNN and SRL graph on the performance of some transformers in computing STS. For the evaluation of our approach, we use STS2017 and SICK datasets. Experimental evaluations show that using the SRL graph accompanied by applying the proposed DGNN increases the performance of the transformers used in the DGNN. |
format | Online Article Text |
id | pubmed-9428166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94281662022-09-01 Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information Mohebbi, Majid Razavi, Seyed Naser Balafar, Mohammad Ali Sci Rep Article We propose a deep graph learning approach for computing semantic textual similarity (STS) by using semantic role labels generated by a Semantic Role Labeling (SRL) system. SRL system output has significant challenges in dealing with graph-neural networks because it doesn't have a graph structure. To address these challenges, we propose a novel SRL graph by using semantic role labels and dependency grammar. For processing the SRL graph, we proposed a Deep Graph Neural Network (DGNN) based graph-U-Net model that is placed on top of the transformers to use a variety of transformers to process representations obtained from them. We investigate the effect of using the proposed DGNN and SRL graph on the performance of some transformers in computing STS. For the evaluation of our approach, we use STS2017 and SICK datasets. Experimental evaluations show that using the SRL graph accompanied by applying the proposed DGNN increases the performance of the transformers used in the DGNN. Nature Publishing Group UK 2022-08-30 /pmc/articles/PMC9428166/ /pubmed/36042274 http://dx.doi.org/10.1038/s41598-022-19259-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Mohebbi, Majid Razavi, Seyed Naser Balafar, Mohammad Ali Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title | Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title_full | Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title_fullStr | Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title_full_unstemmed | Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title_short | Computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
title_sort | computing semantic similarity of texts based on deep graph learning with ability to use semantic role label information |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428166/ https://www.ncbi.nlm.nih.gov/pubmed/36042274 http://dx.doi.org/10.1038/s41598-022-19259-5 |
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