Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
DOI:
https://doi.org/10.1609/aaai.v38i16.29792Keywords:
NLP: Information Extraction, ML: Graph-based Machine LearningAbstract
Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document. Existing approaches rely on logical reasoning or contextual cues from entities. This paper reframes document-level RE as link prediction over a Knowledge Graph (KG) with distinct benefits: 1) Our approach amalgamates entity context and document-derived logical reasoning, enhancing link prediction quality. 2) Predicted links between entities offer interpretability, elucidating employed reasoning. We evaluate our approach on 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 Knowledge Graph link prediction techniques can enhance the performance of document-level relation extraction models.Downloads
Published
2024-03-24
How to Cite
Jain, M., Mutharaju, R., Kavuluru, R., & Singh, K. (2024). Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 18327-18335. https://doi.org/10.1609/aaai.v38i16.29792
Issue
Section
AAAI Technical Track on Natural Language Processing I