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11 mai 2021
- doi: 10.4000/books.aaccademia.7593
- eisbn: 9791280136329
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
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EVALITA COVID-19 Infodemic linguistica computazionale Automatic Misogyny Identification Misogyny on Twitter Posts AlBERTo Convolutional Neural Network BERT Model Hate Speech Detection Multimodal Meme Detection MEME management Language Game ``La Ghigliottina'' Language & Linguistics LAN000000 CBXSujets proches
Language (New words, slang, etc.) Women-hatingCiter ce document
Matteo Brivio, « matteo-brv @ DaDoEval: An SVM-based Approach for Automatic Document Dating », Accademia University Press, ID : 10.4000/books.aaccademia.7593
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Résumé
This paper describes our contribution to the EVALITA 2020 shared task DaDoEval – Dating Document Evaluation. The solution we present is based on a linear multi-class Support Vector Machine classifier trained on a combination of character and word n-grams, as well as number of word tokens per document. Despite its simplicity, the system ranked first both in the coarse-grained classification task on same-genre data and in the one on cross-genre data, achieving a macro-average F1 score of 0.934 and 0.413, respectively. The system implementation is available at https://github.com/matteobrv/DaDoEval.