Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas
Kosuke Akimoto, Takuya Hiraoka, Kunihiko Sadamasa, Mathias Niepert
Abstract
Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, therefore, may suffer from the lack of sufficient relation labels. In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas. To alleviate the sparsity problem and to leverage inherent decomposability of n-ary relations, we propose to learn relation representations of lower-arity facts that result from decomposing higher-arity facts. The proposed method computes a score of a new n-ary fact by aggregating scores of its decomposed lower-arity facts. We conduct experiments with datasets for ternary relation extraction and empirically show that our method improves the n-ary relation extraction performance compared to previous methods.- Anthology ID:
- D19-1645
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6225–6231
- Language:
- URL:
- https://aclanthology.org/D19-1645
- DOI:
- 10.18653/v1/D19-1645
- Cite (ACL):
- Kosuke Akimoto, Takuya Hiraoka, Kunihiko Sadamasa, and Mathias Niepert. 2019. Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6225–6231, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas (Akimoto et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/D19-1645.pdf