UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution

Haonan Li, Minghan Wang, Timothy Baldwin, Martin Tomko, Maria Vasardani


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.
Anthology ID:
S19-2231
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:
1313–1318
Language:
URL:
https://aclanthology.org/S19-2231
DOI:
10.18653/v1/S19-2231
Bibkey:
Cite (ACL):
Haonan Li, Minghan Wang, Timothy Baldwin, Martin Tomko, and Maria Vasardani. 2019. UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1313–1318, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
UniMelb at SemEval-2019 Task 12: Multi-model combination for toponym resolution (Li et al., SemEval 2019)
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PDF:
https://preview.aclanthology.org/author-url/S19-2231.pdf