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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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Abstract:
International audience ; This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian). It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme. Three distinct experimental settings are provided. In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored. In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets. In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture. The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.
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Keyword:
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-TT]Computer Science [cs]/Document and Text Processing
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URL: https://hal.archives-ouvertes.fr/hal-03102480
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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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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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Semantic Web-Mining and Deep Vision for Lifelong Object Discovery
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In: IEEE International Conference on Robotics and Automation (ICRA) ; https://hal.inria.fr/hal-01524902 ; IEEE International Conference on Robotics and Automation (ICRA), May 2017, Singapore, Singapore (2017)
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KNEWS: Using Logical and Lexical Semantics to Extract Knowledge from Natural Language
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In: Proceedings of the European Conference on Artificial Intelligence (ECAI) 2016 conference ; https://hal.inria.fr/hal-01389390 ; Proceedings of the European Conference on Artificial Intelligence (ECAI) 2016 conference, Aug 2016, The Hague, Netherlands (2016)
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