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HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language.

, , , and . WOAH, page 34-43. Association for Computational Linguistics, (2020)

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Answer: approximate Name Search with Errors in Large Databases by a Novel Approach Based on Prefix-dictionary., , , , , and . Int. J. Artif. Intell. Tools, 15 (5): 839-848 (2006)A Scalable and Efficient Outlier Detection Strategy for Categorical Data., , , , and . ICTAI (2), page 210-217. IEEE Computer Society, (2007)0-7695-3015-X.A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes., and . Data Min. Knowl. Discov., 20 (2): 259-289 (2010)Outlier Detection for Large High-Dimensional Categorical Data using Non-Derivable and Non-Almost-Derivable Sets., , , , , and . DMIN, page 505-511. CSREA Press, (2009)HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language., , , and . WOAH, page 34-43. Association for Computational Linguistics, (2020)APHID: An architecture for private, high-performance integrated data mining., , , and . Future Gener. Comput. Syst., 26 (7): 891-904 (2010)How Cloud Computing Is Addressed for Software Development in Computer Science Education., and . HCI (1), volume 10271 of Lecture Notes in Computer Science, page 415-426. Springer, (2017)Deep learning for opinion mining and topic classification of course reviews.. Educ. Inf. Technol., 29 (3): 2973-2997 (February 2024)Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets., , , , and . CoRR, (2023)Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets., , , , and . ICTAI, page 342-346. IEEE, (2022)