Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
Monika Jain, Raghava Mutharaju, Ramakanth Kavuluru, Kuldeep Singh
Abstract
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.
BibTeX
@article{Jain_Mutharaju_Kavuluru_Singh_2024, title={Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29792}, DOI={10.1609/aaai.v38i16.29792}, abstractNote={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.}, number={16}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Jain, Monika and Mutharaju, Raghava and Kavuluru, Ramakanth and Singh, Kuldeep}, year={2024}, month={Mar.}, pages={18327-18335} }