University of

Vikas Yadav, Egoitz Laparra, Ti-Tai Wang, Mihai Surdeanu, Steven Bethard
2019 Proceedings of the 13th International Workshop on Semantic Evaluation  
We present the Named Entity Recognition (NER) and disambiguation model used by the University of Arizona team (UArizona) for 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, prefix 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
more » ... ro 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.
doi:10.18653/v1/s19-2232 dblp:conf/semeval/YadavLWSB19 fatcat:p5azmxawbjerno6f3cj7gle5nq