Document-level Relation Extraction as Semantic Segmentation

doi:10.24963/ijcai.2021/551

Document-level Relation Extraction as Semantic Segmentation

2021-08-01

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DOI：10.24963/ijcai.2021/551

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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.

Document-level Relation Extraction as Semantic Segmentation

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