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HateBERT: Retraining BERT for Abusive Language Detection in English ...
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Multilingual Irony Detection with Dependency Syntax and Neural Models
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In: Proceedings of the 28th International Conference on Computational Linguistics ; 28th International Conference on Computational Linguistics (COLING 2020) ; https://hal.archives-ouvertes.fr/hal-03102480 ; 28th International Conference on Computational Linguistics (COLING 2020), Dec 2020, Barcelona (Online), Spain. pp.1346-1358 ; https://www.aclweb.org/anthology/2020.coling-main.116/ (2020)
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Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection
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In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol 8 No 1 (2020): Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing; 151-154 (2020)
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Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection
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In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol. 8 No. 1 (2020): Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing; 151-154 (2020)
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Abstract:
In this paper we propose an approach to exploit the fine-grained knowledge expressed by individual human annotators during a hate speech (HS) detection task, before the aggregation of single judgments in a gold standard dataset eliminates non-majority perspectives. We automatically divide the annotators into groups, aiming at grouping them by similar personal characteristics (ethnicity, social background, culture etc.). To serve a multi-lingual perspective, we performed classification experiments on three different Twitter datasets in English and Italian languages. We created different gold standards, one for each group, and trained a state-of-the-art deep learning model on them, showing that supervised models informed by different perspectives on the target phenomena outperform a baseline represented by models trained on fully aggregated data. Finally, we implemented an ensemble approach that combines the single perspective-aware classifiers into an inclusive model. The results show that this strategy further improves the classification performance, especially with a significant boost in the recall of HS prediction.
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URL: https://ojs.aaai.org/index.php/HCOMP/article/view/7473
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Multilingual Irony Detection with Dependency Syntax and Neural Models ...
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Multilingual Irony Detection with Dependency Syntax and Neural Models ...
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HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
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EVALITA4ELG: Italian Benchmark Linguistic Resources, NLP Services and Tools for the ELG Platform
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“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media
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Exploring the association between problem drinking and language use on Facebook in young adults
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The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
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14-ExLab@UniTo for AMI at IberEval2018: Exploiting lexical knowledge for detecting misogyny in English and Spanish tweets
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Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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