{"id":475,"count":1,"description":"L'\u00e9tiquetage automatis\u00e9 des textes consiste \u00e0 attribuer des cat\u00e9gories ou des \u00e9tiquettes pr\u00e9d\u00e9finies \u00e0 des donn\u00e9es textuelles \u00e0 l'aide de mod\u00e8les d'intelligence artificielle. Il comprend des t\u00e2ches telles que la classification de documents ou l'extraction d'entit\u00e9s, et fonctionne de mani\u00e8re similaire \u00e0 l'annotation manuelle, mais \u00e0 grande \u00e9chelle et avec beaucoup moins d'efforts. \nCette approche r\u00e9duit consid\u00e9rablement le temps n\u00e9cessaire \u00e0 l'\u00e9tiquetage. Elle peut s'appuyer sur des mod\u00e8les pr\u00e9-entra\u00een\u00e9s (y compris les LLM), des syst\u00e8mes bas\u00e9s sur des r\u00e8gles ou des strat\u00e9gies d'apprentissage actif qui sugg\u00e8rent des \u00e9tiquettes bas\u00e9es sur la confiance du mod\u00e8le et les corrections de l'utilisateur. \nPour garantir des r\u00e9sultats de haute qualit\u00e9, il est important de valider les r\u00e9sultats automatis\u00e9s et de d\u00e9finir au pr\u00e9alable des r\u00e8gles d'annotation claires. Vous trouverez plus d'informations et de bonnes pratiques dans la documentation. 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