Uncertainty-Oriented Textual Marker Selection for Extracting Relevant Terms from Job Offers

Abstract

Automated resume ranking aims at selecting and sorting pertinent resumes, among those sent to answer a given job of er. Most of the screening and elimination process relies on the resumes’ content, marginally including information of the job of er. In this sense, currently available resume ranking approaches lack of accuracy in detecting relevant information in job of ers, which is imperative to assure that selected resumes are pertinent. To improve the extraction of relevant terms that represent significant information in job of ers, we study the uncertainty-oriented selection of 16 textual markers – 10 obtained by examining the behaviour of expert recruiters and 6 from the literature – according to two approaches: fuzzy logistic regression and fuzzy decision trees. Results indicate that globally, fuzzy decision trees improve the F1 and recall metrics, by 27% and 53% respectively, compared to a state-of-the-art term extraction approach.