University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities

Vikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu, Steven Bethard


Abstract
We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for the SemEval 2019 task 12. We achieved fourth place on tasks 1 and 3. We implemented a deep-affix based LSTM-CRF NER model for task 1, which utilizes only character, word, pre- fix and suffix information for the identification of geolocation entities. Despite using just the training data provided by task organizers and not using any lexicon features, we achieved 78.85% strict micro F-score on task 1. We used the unsupervised population heuristics for task 3 and achieved 52.99% strict micro-F1 score in this task.
Anthology ID:
S19-2232
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:
1319–1323
Language:
URL:
https://aclanthology.org/S19-2232
DOI:
10.18653/v1/S19-2232
Bibkey:
Cite (ACL):
Vikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu, and Steven Bethard. 2019. University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1319–1323, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities (Yadav et al., SemEval 2019)
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PDF:
https://aclanthology.org/S19-2232.pdf