Abstract
Relation Discovery discovers predicates (relation types) from a text corpus relying on the co-occurrence of two named entities in the same sentence. This is a very narrowing constraint: it represents only a small fraction of all relation mentions in practice. In this paper we propose a high recall approach for Open IE, which enables covering up to 16 times more sentences in a large corpus. Comparison against OpenIE systems shows that our proposed approach achieves 28% improvement over the highest recall OpenIE system and 6% improvement in precision than the same system.- Anthology ID:
- I17-2039
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 228–233
- Language:
- URL:
- https://aclanthology.org/I17-2039
- DOI:
- Cite (ACL):
- Hady Elsahar, Christophe Gravier, and Frederique Laforest. 2017. High Recall Open IE for Relation Discovery. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 228–233, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- High Recall Open IE for Relation Discovery (Elsahar et al., IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/I17-2039.pdf