Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.24963/ijcai.2021/551 | IJCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ningyu Zhang | 1 | 63 | 18.56 |
| Chen Xiang | 2 | 31 | 35.72 |
| Xin Xie | 3 | 1 | 1.02 |
| Shumin Deng | 4 | 32 | 10.61 |
| Chuanqi Tan | 5 | 29 | 9.25 |
| Mosha Chen | 6 | 2 | 3.50 |
| Fei Huang | 7 | 2 | 7.54 |
| Luo Si | 8 | 2498 | 169.52 |
| Huanhuan Chen | 9 | 731 | 101.79 |