@inproceedings{zhang-etal-2023-novel,
title = "A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction",
author = "Zhang, Ruoyu and
Li, Yanzeng and
Zou, Lei",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.607",
doi = "10.18653/v1/2023.acl-long.607",
pages = "10853--10865",
abstract = "Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. To enhance the learning of task dependencies, TaG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TaG surpasses previous methods by a large margin and achieves state-of-the-art results.",
}
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%0 Conference Proceedings
%T A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction
%A Zhang, Ruoyu
%A Li, Yanzeng
%A Zou, Lei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-novel
%X Document-level relation extraction (DocRE) aims to extract relations among entities within a document, which is crucial for applications like knowledge graph construction. Existing methods usually assume that entities and their mentions are identified beforehand, which falls short of real-world applications. To overcome this limitation, we propose TaG, a novel table-to-graph generation model for joint extractionof entities and relations at document-level. To enhance the learning of task dependencies, TaG induces a latent graph among mentions, with different types of edges indicating different task information, which is further broadcast with a relational graph convolutional network. To alleviate the error propagation problem, we adapt the hierarchical agglomerative clustering algorithm to back-propagate task information at decoding stage. Experiments on the benchmark dataset, DocRED, demonstrate that TaG surpasses previous methods by a large margin and achieves state-of-the-art results.
%R 10.18653/v1/2023.acl-long.607
%U https://aclanthology.org/2023.acl-long.607
%U https://doi.org/10.18653/v1/2023.acl-long.607
%P 10853-10865
Markdown (Informal)
[A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction](https://aclanthology.org/2023.acl-long.607) (Zhang et al., ACL 2023)
ACL