DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets

Gretel Liz De la Peña, Paolo Rosso


Abstract
This paper describes the system we developed for SemEval 2019 on Identifying and Categorizing Offensive Language in Social Media (OffensEval - Task 6). The task focuses on offensive language in tweets. It is organized into three sub-tasks for offensive language identification; automatic categorization of offense types and offense target identification. The approach for the first subtask is a deep learning-based ensemble method which uses a Bidirectional LSTM Recurrent Neural Network and a Convolutional Neural Network. Additionally we use the information from part-of-speech tagging of tweets for target identification and combine previous results for categorization of offense types.
Anthology ID:
S19-2104
Volume:
Proceedings of the 13th International Workshop on Semantic Evaluation
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Editors:
Jonathan May, Ekaterina Shutova, Aurelie Herbelot, Xiaodan Zhu, Marianna Apidianaki, Saif M. Mohammad
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
582–586
Language:
URL:
https://aclanthology.org/S19-2104
DOI:
10.18653/v1/S19-2104
Bibkey:
Cite (ACL):
Gretel Liz De la Peña and Paolo Rosso. 2019. DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 582–586, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Cite (Informal):
DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets (De la Peña & Rosso, SemEval 2019)
Copy Citation:
PDF:
https://aclanthology.org/S19-2104.pdf