Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction

TL;DR

This paper introduces a novel link prediction approach for document-level relation extraction that combines entity context and logical reasoning, improving performance and interpretability on benchmark datasets.

Contribution

It redefines document-level RE as link prediction over a knowledge graph, integrating context and reasoning for better accuracy and explainability.

Findings

Outperforms state-of-the-art models on three datasets

Enhances interpretability through link prediction

Combines logical reasoning with context for improved results

Abstract

Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a knowledge graph with distinct benefits: 1) Our approach combines entity context with document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on three benchmark datasets: DocRED, ReDocRED, and DWIE. The results indicate that our proposed method outperforms the state-of-the-art models and suggests that incorporating context-based link prediction techniques can enhance the performance of document-level relation extraction models.