COLING 2020main13 citations
TIMBERT: Toponym Identifier For The Medical Domain Based on BERT
MohammadReza Davari, Leila Kosseim, Tien Bui
Abstract
In this paper, we propose an approach to automate the process of place name detection in the medical domain to enable epidemiologists to better study and model the spread of viruses. We created a family of Toponym Identification Models based on BERT (TIMBERT), in order to learn in an end-to-end fashion the mapping from an input sentence to the associated sentence labeled with toponyms. When evaluated with the SemEval 2019 task 12 test set (Weissenbacher et al., 2019), our best TIMBERT model achieves an F1 score of 90.85%, a significant improvement compared to the state-of-the-art of 89.13% (Wang et al., 2019).
BibTeX
@inproceedings{davari-etal-2020-timbert,
title = "{TIMBERT}: Toponym Identifier For The Medical Domain Based on {BERT}",
author = "Davari, MohammadReza and
Kosseim, Leila and
Bui, Tien",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.58/",
doi = "10.18653/v1/2020.coling-main.58",
pages = "662--668"
}