Document-level Relation Extraction as Semantic Segmentation

Zhang, Ningyu; Chen, Xiang; Xie, Xin; Deng, Shumin; Tan, Chuanqi; Chen, Mosha; Huang, Fei; Si, Luo; Chen, Huajun

doi:10.24963/ijcai.2021/551

Document-level Relation Extraction as Semantic Segmentation

Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3999-4006.

https://doi.org/10.24963/ijcai.2021/551

Keywords

Natural Language Processing: Information Extraction
Natural Language Processing: Knowledge Extraction

Abstract

Document-level relation extraction aims to extract relations among multiple entity pairs from a document. Previously proposed graph-based or transformer-based models utilize the entities independently, regardless of global information among relational triples. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information, parallel to the semantic segmentation task in computer vision. Herein, we propose a Document U-shaped Network for document-level relation extraction. Specifically, we leverage an encoder module to capture the context information of entities and a U-shaped segmentation module over the image-style feature map to capture global interdependency among triples. Experimental results show that our approach can obtain state-of-the-art performance on three benchmark datasets DocRED, CDR, and GDA.

Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, and Huajun Chen. 2021. Document-level Relation Extraction as Semantic Segmentation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3999–4006. https://doi.org/10.24963/ijcai.2021/551

@inproceedings{ijcai2021p551,
  title     = {Document-level Relation Extraction as Semantic Segmentation},
  author    = {Zhang, Ningyu and Chen, Xiang and Xie, Xin and Deng, Shumin and Tan, Chuanqi and Chen, Mosha and Huang, Fei and Si, Luo and Chen, Huajun},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {3999--4006},
  year      = {2021},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2021/551},
  url       = {https://doi.org/10.24963/ijcai.2021/551},
}

TY  - CPAPER
TI  - Document-level Relation Extraction as Semantic Segmentation
AU  - Zhang, Ningyu
AU  - Chen, Xiang
AU  - Xie, Xin
AU  - Deng, Shumin
AU  - Tan, Chuanqi
AU  - Chen, Mosha
AU  - Huang, Fei
AU  - Si, Luo
AU  - Chen, Huajun
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 3999
EP  - 4006
DO  - 10.24963/ijcai.2021/551
UR  - https://doi.org/10.24963/ijcai.2021/551
ER  -