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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/N18-5009.pdf