@InProceedings{yadav-EtAl:2019:S19-2,
  author    = {Yadav, Vikas  and  Laparra, Egoitz  and  Wang, Ti-Tai  and  Surdeanu, Mihai  and  Bethard, Steven},
  title     = {University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities},
  booktitle = {Proceedings of the 13th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2019},
  address   = {Minneapolis, Minnesota, USA},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/S19-2232}
}