@inproceedings{yadav-etal-2019-university,
title = "{U}niversity of {A}rizona at {S}em{E}val-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities",
author = "Yadav, Vikas and
Laparra, Egoitz and
Wang, Ti-Tai and
Surdeanu, Mihai and
Bethard, Steven",
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/S19-2232",
doi = "10.18653/v1/S19-2232",
pages = "1319--1323",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities
%A Yadav, Vikas
%A Laparra, Egoitz
%A Wang, Ti-Tai
%A Surdeanu, Mihai
%A Bethard, Steven
%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 yadav-etal-2019-university
%X 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.
%R 10.18653/v1/S19-2232
%U https://aclanthology.org/S19-2232
%U https://doi.org/10.18653/v1/S19-2232
%P 1319-1323
Markdown (Informal)
[University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities](https://aclanthology.org/S19-2232) (Yadav et al., SemEval 2019)
ACL