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ltl.uni-due at SemEval 2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter

In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexico-semantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.

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Conference:
NAACL HLT 2019, 13th International Workshop on Semantic Evaluation (SemEval 2019), June 6–June 7, 2019 Minneapolis, Minnesota, USA
Date Issued:
2019
URN:
urn:nbn:de:hbz:464-20211018-140300-1
DOI:
10.18653/v1/S19-2078
Language:
English
Type of Resource:
Text
Collection:
E-Publications
Subject categories of the Deutsche Nationalbibliografie:
004 Data processing Computer sciences
Hosting institution:
Faculty of Engineering, Computer Science and Applied Cognitive Science, Computer Science, Language Technology Lab
accrual policy:
Zhang, H., Wojatzki, M., Horsmann, T., & Zesch, T. (2019). ltl.uni-due at SemEval 2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval 2019), pp. 441–446. Association for Computational Linguistics. DOI: https://doi.org/10.18653/v1/S19-2078

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