Papers by Jood Otey
Representing and Clustering Errors in Offensive Language Detection (2025.naacl-srw)
Copied to clipboard
| Challenge: | Sentence-BERT embeddings of Large Language Model (LLM)-generated linguistic features give the most interpretable clustering for Arabic errors. |
| Approach: | They evaluate the K-Means clustering of four text representations for the task of offensive language detection in English and Levantine Arabic. |
| Outcome: | The proposed clustering of four text representations for offensive language detection in English and Levantine Arabic gives the most human-interpretable clustering for English errors and the grouping is mainly based on the targeted group in the text. |