@inproceedings{yang-etal-2023-learning-leverage,
title = "Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction",
author = "Yang, Zhe and
Huang, Yi and
Feng, Junlan",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.575",
doi = "10.18653/v1/2023.findings-acl.575",
pages = "9023--9035",
abstract = "Automatic medical entity and relation extraction is essential for daily electronic medical record (EMR) analysis, and has attracted a lot of academic attention. Tremendous progress has been made in recent years. However, medical terms are difficult to understand, and their relations are more complicated than general ones. Based on this situation, domain knowledge gives better background and contexts for medical terms. Despite the benefits of medical domain knowledge, the utilization way of it for joint entity and relation extraction is inadequate. To foster this line of research, in this work, we propose to leverage the medical knowledge graph for extracting entities and relations for Chinese Medical Texts in a collective way. Specifically, we propose to construct a high-order heterogeneous graph based on medical knowledge graph, which is linked to the entity mentions in the text. In this way, neighbors from the high-order heterogeneous graph can pass the message to each other for better global context representations. Our experiments on real Chinese Medical Texts show that our method is more effective than state-of-the-art methods.",
}
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<abstract>Automatic medical entity and relation extraction is essential for daily electronic medical record (EMR) analysis, and has attracted a lot of academic attention. Tremendous progress has been made in recent years. However, medical terms are difficult to understand, and their relations are more complicated than general ones. Based on this situation, domain knowledge gives better background and contexts for medical terms. Despite the benefits of medical domain knowledge, the utilization way of it for joint entity and relation extraction is inadequate. To foster this line of research, in this work, we propose to leverage the medical knowledge graph for extracting entities and relations for Chinese Medical Texts in a collective way. Specifically, we propose to construct a high-order heterogeneous graph based on medical knowledge graph, which is linked to the entity mentions in the text. In this way, neighbors from the high-order heterogeneous graph can pass the message to each other for better global context representations. Our experiments on real Chinese Medical Texts show that our method is more effective than state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction
%A Yang, Zhe
%A Huang, Yi
%A Feng, Junlan
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F yang-etal-2023-learning-leverage
%X Automatic medical entity and relation extraction is essential for daily electronic medical record (EMR) analysis, and has attracted a lot of academic attention. Tremendous progress has been made in recent years. However, medical terms are difficult to understand, and their relations are more complicated than general ones. Based on this situation, domain knowledge gives better background and contexts for medical terms. Despite the benefits of medical domain knowledge, the utilization way of it for joint entity and relation extraction is inadequate. To foster this line of research, in this work, we propose to leverage the medical knowledge graph for extracting entities and relations for Chinese Medical Texts in a collective way. Specifically, we propose to construct a high-order heterogeneous graph based on medical knowledge graph, which is linked to the entity mentions in the text. In this way, neighbors from the high-order heterogeneous graph can pass the message to each other for better global context representations. Our experiments on real Chinese Medical Texts show that our method is more effective than state-of-the-art methods.
%R 10.18653/v1/2023.findings-acl.575
%U https://aclanthology.org/2023.findings-acl.575
%U https://doi.org/10.18653/v1/2023.findings-acl.575
%P 9023-9035
Markdown (Informal)
[Learning to Leverage High-Order Medical Knowledge Graph for Joint Entity and Relation Extraction](https://aclanthology.org/2023.findings-acl.575) (Yang et al., Findings 2023)
ACL