University of Arizona at SemEval-2019 Task 12: Deep-Affix Named Entity Recognition of Geolocation Entities
| Autor: | Vikas Yadav, Ti-Tai Wang, Steven Bethard, Egoitz Laparra, Mihai Surdeanu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science business.industry Affix 02 engineering and technology computer.software_genre Lexicon SemEval Task (project management) 03 medical and health sciences Identification (information) Geolocation 030104 developmental biology Named-entity recognition 020204 information systems 0202 electrical engineering electronic engineering information engineering Artificial intelligence Suffix business computer Natural language processing |
| Zdroj: | SemEval@NAACL-HLT |
| DOI: | 10.18653/v1/s19-2232 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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