Abstract
Unsupervised relation discovery aims to discover new relations from a given text corpus without annotated data. However, it does not consider existing human annotated knowledge bases even when they are relevant to the relations to be discovered. In this paper, we study the problem of how to use out-of-relation knowledge bases to supervise the discovery of unseen relations, where out-of-relation means that relations to discover from the text corpus and those in knowledge bases are not overlapped. We construct a set of constraints between entity pairs based on the knowledge base embedding and then incorporate constraints into the relation discovery by a variational auto-encoder based algorithm. Experiments show that our new approach can improve the state-of-the-art relation discovery performance by a large margin.- Anthology ID:
- N19-1332
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3280–3290
- Language:
- URL:
- https://aclanthology.org/N19-1332
- DOI:
- 10.18653/v1/N19-1332
- Cite (ACL):
- Yan Liang, Xin Liu, Jianwen Zhang, and Yangqiu Song. 2019. Relation Discovery with Out-of-Relation Knowledge Base as Supervision. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3280–3290, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Relation Discovery with Out-of-Relation Knowledge Base as Supervision (Liang et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/N19-1332.pdf
- Code
- HKUST-KnowComp/RE-RegDVAE