Abstract
This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classi- fication where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguis- tic features to improve the classifier’s perfor- mance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency- Inverse Document Frequency (TF-IDF) repre- sentation. The system performance reaches about 81% F1 when it is applied to the training dataset, but its F1 drops to 36% on the official test dataset for the English and 64% for the Spanish language concerning the hate speech class- Anthology ID:
- S19-2068
- Volume:
- Proceedings of the 13th International Workshop on Semantic Evaluation
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota, USA
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 387–390
- Language:
- URL:
- https://aclanthology.org/S19-2068
- DOI:
- 10.18653/v1/S19-2068
- Cite (ACL):
- Sattam Almatarneh, Pablo Gamallo, and Francisco J. Ribadas Pena. 2019. CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 387–390, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
- Cite (Informal):
- CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets (Almatarneh et al., SemEval 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/S19-2068.pdf