RGCL-WLV at SemEval-2019 Task 12: Toponym Detection

Alistair Plum, Tharindu Ranasinghe, Pablo Calleja, Constantin Orăsan, Ruslan Mitkov


Abstract
This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers. The system detects toponyms using a bootstrapped machine learning (ML) approach which classifies names identified using gazetteers extracted from the GeoNames geographical database. The paper evaluates the performance of several ML classifiers, as well as how the gazetteers influence the accuracy of the system. Several runs were submitted. The highest precision achieved for one of the submissions was 89%, albeit it at a relatively low recall of 49%.
Anthology ID:
S19-2228
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1297–1301
Language:
URL:
https://aclanthology.org/S19-2228
DOI:
10.18653/v1/S19-2228
Bibkey:
Cite (ACL):
Alistair Plum, Tharindu Ranasinghe, Pablo Calleja, Constantin Orăsan, and Ruslan Mitkov. 2019. RGCL-WLV at SemEval-2019 Task 12: Toponym Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1297–1301, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
RGCL-WLV at SemEval-2019 Task 12: Toponym Detection (Plum et al., SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2228.pdf