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11 mai 2021
- doi: 10.4000/books.aaccademia.7678
- 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
Jens Kaiser et al., « OP-IMS @ DIACR-Ita: Back to the Roots: SGNS+OP+CD still Rocks Semantic Change Detection », Accademia University Press, ID : 10.4000/books.aaccademia.7678
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Résumé
We present the results of our participation in the DIACR-Ita shared task on lexical semantic change detection for Italian. We exploit one of the earliest and most influential semantic change detection models based on Skip-Gram with Negative Sampling, Orthogonal Procrustes alignment and Cosine Distance and obtain the winning submission of the shared task with near to perfect accuracy (.94). Our results once more indicate that, within the present task setup in lexical semantic change detection, the traditional type-based approaches yield excellent performance.