University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities

Vikas Yadav; Egoitz Laparra; Ti Tai Wang; Mihai Surdeanu; Steven Bethard

University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities

Vikas Yadav, Egoitz Laparra, Ti Tai Wang, Mihai Surdeanu, Steven Bethard

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 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.

Original language	English (US)
Title of host publication	NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop
Publisher	Association for Computational Linguistics (ACL)
Pages	1319-1323
Number of pages	5
ISBN (Electronic)	9781950737062
State	Published - 2019
Event	13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States Duration: Jun 6 2019 → Jun 7 2019

Publication series

Name	NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop

Conference

Conference	13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Country/Territory	United States
City	Minneapolis
Period	6/6/19 → 6/7/19

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Science Applications
Theoretical Computer Science

Cite this

Yadav, V., Laparra, E., Wang, T. T., Surdeanu, M., & Bethard, S. (2019). University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop (pp. 1319-1323). (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop). Association for Computational Linguistics (ACL).

University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities. / Yadav, Vikas; Laparra, Egoitz; Wang, Ti Tai et al.
NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL), 2019. p. 1319-1323 (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yadav, V, Laparra, E, Wang, TT, Surdeanu, M & Bethard, S 2019, University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities. in NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop, Association for Computational Linguistics (ACL), pp. 1319-1323, 13th International Workshop on Semantic Evaluation, SemEval 2019, co-located with the 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019, Minneapolis, United States, 6/6/19.

Yadav V, Laparra E, Wang TT, Surdeanu M , Bethard S. University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities. In NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL). 2019. p. 1319-1323. (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop).

Yadav, Vikas ; Laparra, Egoitz ; Wang, Ti Tai et al. / University of Arizona at SemEval-2019 task 12 : Deep-affix named entity recognition of geolocation entities. NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop. Association for Computational Linguistics (ACL), 2019. pp. 1319-1323 (NAACL HLT 2019 - International Workshop on Semantic Evaluation, SemEval 2019, Proceedings of the 13th Workshop).

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abstract = "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 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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University of Arizona at SemEval-2019 task 12: Deep-affix named entity recognition of geolocation entities

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