Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
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.
Cite
Text
Jain et al. "Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I16.29792Markdown
[Jain et al. "Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/jain2024aaai-revisiting/) doi:10.1609/AAAI.V38I16.29792BibTeX
@inproceedings{jain2024aaai-revisiting,
title = {{Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction}},
author = {Jain, Monika and Mutharaju, Raghava and Kavuluru, Ramakanth and Singh, Kuldeep},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {18327-18335},
doi = {10.1609/AAAI.V38I16.29792},
url = {https://mlanthology.org/aaai/2024/jain2024aaai-revisiting/}
}