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Article dans une revue

Procode: A Machine-Learning Tool to Support (Re-)coding of Free-Texts of Occupations and Industries

Abstract : Abstract Procode is a free of charge web-tool that allows automatic coding of occupational data (free-texts) by implementing Complement Naïve Bayes (CNB) as a machine-learning technique. The paper describes the algorithm, performance evaluation, and future goals regarding the tool’s development. Almost 30 000 free-texts with manually assigned classification codes of French classification of occupations (PCS) and French classification of activities (NAF) were used to train CNB. A 5-fold cross-validation found that Procode predicts correct classification codes in 57–81 and 63–83% cases for PCS and NAF, respectively. Procode also integrates recoding between two classifications. In the first version of Procode, this operation, however, is only a simple search function of recoding links in existing crosswalks. Future focus of the project will be collection of the data to support automatic coding to other classification and to establish a more advanced method for recoding.
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https://hal.univ-angers.fr/hal-03345693
Contributeur : Fabien Gilbert Connectez-vous pour contacter le contributeur
Soumis le : mercredi 15 septembre 2021 - 17:22:17
Dernière modification le : mercredi 30 mars 2022 - 14:42:57

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Nenad Savic, Nicolas Bovio, Fabien Gilbert, José Paz, Irina Guseva Canu. Procode: A Machine-Learning Tool to Support (Re-)coding of Free-Texts of Occupations and Industries. Annals of Work Exposures and Health, Oxford University Press, 2021, ⟨10.1093/annweh/wxab037⟩. ⟨hal-03345693⟩

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