Enhancing Document-level Relation Extraction by Entity Knowledge Injection

• URL: http://arxiv.org/abs/2207.11433v1
• Date: Sat, 23 Jul 2022 06:45:11 GMT
• Title: Enhancing Document-level Relation Extraction by Entity Knowledge Injection
• Authors: Xinyi Wang and Zitao Wang and Weijian Sun and Wei Hu
• Abstract summary: Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. We propose an entity knowledge injection framework to enhance current document-level RE models.
• Score: 33.35887114768141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.

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