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Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus ...
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Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement ...
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3
Fine-Grained Fairness Analysis of Abusive Language Detection Systems with CheckList ...
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4
FrameNet-like Annotation of Olfactory Information in Texts ...
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5
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus ...
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6
A Smell is Worth a Thousand Words: Olfactory Information Extraction and Semantic Processing in a Multilingual Perspective (Invited Talk) ...
Tonelli, Sara. - : Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2021
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7
Hybrid Emoji-Based Masked Language Models for Zero-Shot Abusive Language Detection
In: EMNLP 2020 - Conference on Empirical Methods in Natural Language Processing ; https://hal.archives-ouvertes.fr/hal-02972203 ; EMNLP 2020 - Conference on Empirical Methods in Natural Language Processing, Nov 2020, Virtual, France (2020)
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8
A Multilingual Evaluation for Online Hate Speech Detection
In: ISSN: 1533-5399 ; ACM Transactions on Internet Technology ; https://hal.archives-ouvertes.fr/hal-02972184 ; ACM Transactions on Internet Technology, Association for Computing Machinery, 2020, 20 (2), pp.1-22. ⟨10.1145/3377323⟩ (2020)
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9
Creating a Multimodal Dataset of Images and Text to Study Abusive Language ...
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10
Cross-Platform Evaluation for Italian Hate Speech Detection
In: CLiC-it 2019 - 6th Annual Conference of the Italian Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02381152 ; CLiC-it 2019 - 6th Annual Conference of the Italian Association for Computational Linguistics, Nov 2019, Bari, Italy (2019)
Abstract: International audience ; English. Despite the number of approaches recently proposed in NLP for detecting abusive language on social networks , the issue of developing hate speech detection systems that are robust across different platforms is still an unsolved problem. In this paper we perform a comparative evaluation on datasets for hate speech detection in Italian, extracted from four different social media platforms, i.e. Facebook, Twitter, Instagram and What-sApp. We show that combining such platform-dependent datasets to take advantage of training data developed for other platforms is beneficial, although their impact varies depending on the social network under consideration. 1 Italiano. Nonostante si osservi un cre-scente interesse per approcci che identi-fichino il linguaggio offensivo sui social network attraverso l'NLP, la necessità di sviluppare sistemi che mantengano una buona performance anche su piattaforme diverseè ancora un tema di ricerca aper-to. In questo contributo presentiamo una valutazione comparativa su dataset per l'identificazione di linguaggio d'odio pro-venienti da quattro diverse piattaforme: Facebook, Twitter, Instagram and Wha-tsApp. Lo studio dimostra che, combinan-do dataset diversi per aumentare i dati di training, migliora le performance di clas-sificazione, anche se l'impatto varia a se-conda della piattaforma considerata. 1
Keyword: [SCCO.COMP]Cognitive science/Computer science
URL: https://hal.archives-ouvertes.fr/hal-02381152
https://hal.archives-ouvertes.fr/hal-02381152/file/paper22.pdf
https://hal.archives-ouvertes.fr/hal-02381152/document
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11
Enhancing statistical machine translation with bilingual terminology in a CAT environment
Arcan, Mihael; Turchi, Marco; Tonelli, Sara. - : Association for Machine Translation in the Americas, 2019
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12
SIMPITIKI corpus for simplification in Italian ...
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13
SIMPITIKI corpus for simplification in Italian ...
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14
SIMPITIKI corpus for simplification in Italian ...
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15
The impact of phrases on Italian lexical simplification ...
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16
Italian Lexical Simplification Benchmark ...
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17
The impact of phrases on Italian lexical simplification ...
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18
Italian Lexical Simplification Benchmark ...
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19
Never Retreat, Never Retract: Argumentation Analysis for Political Speeches
In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence ; AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-01876442 ; AAAI 2018 - 32nd AAAI Conference on Artificial Intelligence, Feb 2018, New Orleans, United States. pp.4889-4896 ; https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16393 (2018)
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20
InriaFBK at Germeval 2018: Identifying Offensive Tweets Using Recurrent Neural Networks
In: http://hw.oeaw.ac.at/8435-5 (2018)
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