Abstract
Knowledge Bases (KBs) require constant updating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.- Anthology ID:
- N19-1323
- 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
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3201–3206
- Language:
- URL:
- https://aclanthology.org/N19-1323
- DOI:
- 10.18653/v1/N19-1323
- Cite (ACL):
- Peng Xu and Denilson Barbosa. 2019. Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction. 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 3201–3206, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (Xu & Barbosa, NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/N19-1323.pdf
- Code
- billy-inn/HRERE