Knowledge-Driven Cross-Document Relation Extraction
Monika Jain, Raghava Mutharaju, Kuldeep Singh, Ramakanth Kavuluru
Abstract
Relation extraction (RE) is a well-known NLP application often treated as a sentence or document-level task. However, a handful of recent efforts explore it across documents or in the cross-document setting (CrossDocRE). This is distinct from the single document case because different documents often focus on disparate themes, while text within a document tends to have a single goal.Current CrossDocRE efforts do not consider domain knowledge, which are often assumed to be known to the reader when documents are authored. Here, we propose a novel approach, KXDocRE, that embed domain knowledge of entities with input text for cross-document RE. Our proposed framework has three main benefits over baselines: 1) it incorporates domain knowledge of entities along with documents’ text; 2) it offers interpretability by producing explanatory text for predicted relations between entities 3) it improves performance over the prior methods. Code and models are available at https://github.com/kracr/cross-doc-relation-extraction.- Anthology ID:
- 2024.findings-acl.227
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2024
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Lun-Wei Ku, Andre Martins, Vivek Srikumar
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3787–3797
- Language:
- URL:
- https://aclanthology.org/2024.findings-acl.227
- DOI:
- 10.18653/v1/2024.findings-acl.227
- Cite (ACL):
- Monika Jain, Raghava Mutharaju, Kuldeep Singh, and Ramakanth Kavuluru. 2024. Knowledge-Driven Cross-Document Relation Extraction. In Findings of the Association for Computational Linguistics: ACL 2024, pages 3787–3797, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge-Driven Cross-Document Relation Extraction (Jain et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.227.pdf