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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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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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Abstract:
Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in sev- eral languages. We report on our experience in the organization of SENTIPOLC (SENTIment POLarity Classification Task), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets – which include an enriched annotation scheme for dealing with the impact of figurative language on polarity – the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions.
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Keyword:
Evaluation; Irony Detection; Sentiment Analysis; Social Media Analysis
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URL: http://hdl.handle.net/2318/1684800 https://doi.org/10.1109/TAFFC.2018.2884015 http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165369
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