Abstract
This paper describes the Duluth systems that participated in SemEval-2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black-list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.
| Original language | English (US) |
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| 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 | 593-599 |
| Number of pages | 7 |
| 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 |
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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 |
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| Country/Territory | United States |
| City | Minneapolis |
| Period | 6/6/19 → 6/7/19 |
Bibliographical note
Publisher Copyright:© 2019 Association for Computational Linguistics