Studo jobs: Enriching data with predicted Job labels

Markus Reiter-Haas, Valentin Slawicek, Emanuel Lacic

Publikation: Beitrag in einer FachzeitschriftKonferenzartikelBegutachtung

Abstract

In this paper, we present the Studo Jobs platform in which we tackle the problem of automatically assigning labels to new job advertisements. For that purpose we perform an exhaustive comparison study of state-of-the-art classifiers to be used for label prediction in the job domain. Our findings suggest that in most cases an SVM based approach using stochastic gradient descent performs best on the textual content of job advertisements in terms of Accuracy, F1-measure and AUC. Consequently, we plan to use the best performing classifier for each label which is relevant to the Studo Jobs platform in order to automatically enrich the job advertisement data. We believe that our work is of interest for both researchers and practitioners in the area of automatic labeling and enriching text-based data.

Originalspracheenglisch
FachzeitschriftCEUR Workshop Proceedings
Jahrgang2025
PublikationsstatusVeröffentlicht - 1 Jan. 2017
Veranstaltung17th International Conference on Knowledge Technologies and Data-Driven Business: i-KNOW 2017 - Messezentrum Graz, Graz, Österreich
Dauer: 11 Okt. 201712 Okt. 2017

ASJC Scopus subject areas

  • Informatik (insg.)

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