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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% strictdoi:10.18653/v1/s19-2232 dblp:conf/semeval/YadavLWSB19 fatcat:p5azmxawbjerno6f3cj7gle5nq