Linked-DocRED - Enhancing DocRED with Entity-Linking to Evaluate End-To-End Document-Level Information Extraction Pipelines-Reference-Cited by-同舟云学术

Linked-DocRED - Enhancing DocRED with Entity-Linking to Evaluate End-To-End Document-Level Information Extraction Pipelines

Published:2023-07-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Genest Pierre-Yves¹^ORCID,Portier Pierre-Edouard²^ORCID,Egyed-Zsigmond Elöd²^ORCID,Lovisetto Martino³^ORCID

Affiliation:

1. Alteca & Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Lyon, France

2. Univ Lyon, INSA Lyon, CNRS, UCBL, LIRIS, UMR5205, Villeurbanne, France

3. Alteca, Lyon, France

Funder

Alteca

Association Nationale de la Recherche et de la Technologie

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539618.3591912

Reference44 articles.

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5. Qiao Cheng , Juntao Liu , Xiaoye Qu , Jin Zhao , Jiaqing Liang , Zhefeng Wang , Baoxing Huai , Nicholas Jing Yuan, and Yanghua Xiao . 2021 . HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics , Online, 2819--2831. https://doi.org/10.18653/v1/2021.findings-acl.249 10.18653/v1 Qiao Cheng, Juntao Liu, Xiaoye Qu, Jin Zhao, Jiaqing Liang, Zhefeng Wang, Baoxing Huai, Nicholas Jing Yuan, and Yanghua Xiao. 2021. HacRED: A Large-Scale Relation Extraction Dataset Toward Hard Cases in Practical Applications. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, 2819--2831. https://doi.org/10.18653/v1/2021.findings-acl.249