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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/S19-2104.pdf