@inproceedings{li-etal-2019-unimelb,
title = "{U}ni{M}elb at {S}em{E}val-2019 Task 12: Multi-model combination for toponym resolution",
author = "Li, Haonan and
Wang, Minghan and
Baldwin, Timothy and
Tomko, Martin and
Vasardani, Maria",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2231",
doi = "10.18653/v1/S19-2231",
pages = "1313--1318",
abstract = "This paper describes our submission to SemEval-2019 Task 12 on toponym resolution over scientific articles. We train separate NER models for toponym detection over text extracted from tables vs. text from the body of the paper, and train another auxiliary model to eliminate misdetected toponyms. For toponym disambiguation, we use an SVM classifier with hand-engineered features. The best setting achieved a strict micro-F1 score of 80.92{\%} and overlap micro-F1 score of 86.88{\%} in the toponym detection subtask, ranking 2nd out of 8 teams on F1 score. For toponym disambiguation and end-to-end resolution, we officially ranked 2nd and 3rd, respectively.",
}
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%0 Conference Proceedings
%T UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution
%A Li, Haonan
%A Wang, Minghan
%A Baldwin, Timothy
%A Tomko, Martin
%A Vasardani, Maria
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F li-etal-2019-unimelb
%X This paper describes our submission to SemEval-2019 Task 12 on toponym resolution over scientific articles. We train separate NER models for toponym detection over text extracted from tables vs. text from the body of the paper, and train another auxiliary model to eliminate misdetected toponyms. For toponym disambiguation, we use an SVM classifier with hand-engineered features. The best setting achieved a strict micro-F1 score of 80.92% and overlap micro-F1 score of 86.88% in the toponym detection subtask, ranking 2nd out of 8 teams on F1 score. For toponym disambiguation and end-to-end resolution, we officially ranked 2nd and 3rd, respectively.
%R 10.18653/v1/S19-2231
%U https://aclanthology.org/S19-2231
%U https://doi.org/10.18653/v1/S19-2231
%P 1313-1318
Markdown (Informal)
[UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution](https://aclanthology.org/S19-2231) (Li et al., SemEval 2019)
ACL