Hypergraph based Understanding for Document Semantic Entity Recognition

Qiwei Li, Zuchao Li, Ping Wang, Haojun Ai, Hai Zhao

Abstract
Semantic entity recognition is an important task in the field of visually-rich document understanding. It distinguishes the semantic types of text by analyzing the position relationship between text nodes and the relation between text content. The existing document understanding models mainly focus on entity categories while ignoring the extraction of entity boundaries. We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. It can conduct a more detailed analysis of the document text representation analyzed by the upstream model and achieves a better performance of semantic information. We apply this method on the basis of GraphLayoutLM to construct a new semantic entity recognition model HGALayoutLM. Our experiment results on FUNSD, CORD, XFUND and SROIE show that our method can effectively improve the performance of semantic entity recognition tasks based on the original model. The results of HGALayoutLM on FUNSD and XFUND reach the new state-of-the-art results.
Anthology ID:
2024.acl-long.162
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2950–2960
Language:
URL:
https://aclanthology.org/2024.acl-long.162
DOI:
Bibkey:
Cite (ACL):
Qiwei Li, Zuchao Li, Ping Wang, Haojun Ai, and Hai Zhao. 2024. Hypergraph based Understanding for Document Semantic Entity Recognition. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2950–2960, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Hypergraph based Understanding for Document Semantic Entity Recognition (Li et al., ACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.acl-long.162.pdf
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