ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, Heng Ji
Abstract
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap. We make all of our data sets, resources and system training and testing APIs publicly available for research purpose.- Anthology ID:
- N18-5009
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Yang Liu, Tim Paek, Manasi Patwardhan
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 41–45
- Language:
- URL:
- https://aclanthology.org/N18-5009
- DOI:
- 10.18653/v1/N18-5009
- Cite (ACL):
- Boliang Zhang, Ying Lin, Xiaoman Pan, Di Lu, Jonathan May, Kevin Knight, and Heng Ji. 2018. ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, pages 41–45, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System (Zhang et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/N18-5009.pdf