Document-level Relation Extraction as Semantic Segmentation

• URL: http://arxiv.org/abs/2106.03618v1
• Date: Mon, 7 Jun 2021 13:44:44 GMT
• Title: Document-level Relation Extraction as Semantic Segmentation
• Authors: Ningyu Zhang, Xiang Chen, Xin Xie, Shumin Deng, Chuanqi Tan, Mosha Chen, Fei Huang, Luo Si, Huajun Chen
• Abstract summary: Document-level relation extraction aims to extract relations among multiple entity pairs from a document. This paper approaches the problem by predicting an entity-level relation matrix to capture local and global information. We propose a Document U-shaped Network for document-level relation extraction.
• Score: 38.614931876015625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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