Abstract
Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across paragraphs. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN), a method to recognize such relations for long paragraphs. GAIN constructs two graphs, a heterogeneous mention-level graph (MG) and an entity-level graph (EG). The former captures complex interaction among different mentions and the latter aggregates mentions underlying for the same entities. Based on the graphs we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/PKUnlp-icler/GAIN.- Anthology ID:
- 2020.emnlp-main.127
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1630–1640
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.127
- DOI:
- 10.18653/v1/2020.emnlp-main.127
- Cite (ACL):
- Shuang Zeng, Runxin Xu, Baobao Chang, and Lei Li. 2020. Double Graph Based Reasoning for Document-level Relation Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1630–1640, Online. Association for Computational Linguistics.
- Cite (Informal):
- Double Graph Based Reasoning for Document-level Relation Extraction (Zeng et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.emnlp-main.127.pdf
- Code
- DreamInvoker/GAIN + additional community code
- Data
- DocRED