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Bidirectional matching and aggregation network for few-shot relation extraction
Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280494/ https://www.ncbi.nlm.nih.gov/pubmed/37346532 http://dx.doi.org/10.7717/peerj-cs.1272 |
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author | Wei, Zhongcheng Guo, Wenjie Zhang, Yunping Zhang, Jieying Zhao, Jijun |
author_facet | Wei, Zhongcheng Guo, Wenjie Zhang, Yunping Zhang, Jieying Zhao, Jijun |
author_sort | Wei, Zhongcheng |
collection | PubMed |
description | Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN. |
format | Online Article Text |
id | pubmed-10280494 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804942023-06-21 Bidirectional matching and aggregation network for few-shot relation extraction Wei, Zhongcheng Guo, Wenjie Zhang, Yunping Zhang, Jieying Zhao, Jijun PeerJ Comput Sci Artificial Intelligence Few-shot relation extraction is used to solve the problem of long tail distribution of data by matching between query instances and support instances. Existing methods focus only on the single direction process of matching, ignoring the symmetry of the data in the process. To address this issue, we propose the bidirectional matching and aggregation network (BMAN), which is particularly powerful when the training data is symmetrical. This model not only tries to extract relations for query instances, but also seeks relational prototypes about the query instances to validate the feature representation of the support set. Moreover, to avoid overfitting in bidirectional matching, the data enhancement method was designed to scale up the number of instances while maintaining the scope of the instance relation class. Extensive experiments on FewRel and FewRel2.0 public datasets are conducted and evaluate the effectiveness of BMAN. PeerJ Inc. 2023-03-06 /pmc/articles/PMC10280494/ /pubmed/37346532 http://dx.doi.org/10.7717/peerj-cs.1272 Text en © 2023 Wei et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Artificial Intelligence Wei, Zhongcheng Guo, Wenjie Zhang, Yunping Zhang, Jieying Zhao, Jijun Bidirectional matching and aggregation network for few-shot relation extraction |
title | Bidirectional matching and aggregation network for few-shot relation extraction |
title_full | Bidirectional matching and aggregation network for few-shot relation extraction |
title_fullStr | Bidirectional matching and aggregation network for few-shot relation extraction |
title_full_unstemmed | Bidirectional matching and aggregation network for few-shot relation extraction |
title_short | Bidirectional matching and aggregation network for few-shot relation extraction |
title_sort | bidirectional matching and aggregation network for few-shot relation extraction |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280494/ https://www.ncbi.nlm.nih.gov/pubmed/37346532 http://dx.doi.org/10.7717/peerj-cs.1272 |
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