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Inconsistency Detection in Job Postings

Authors Joana Urbano , Miguel Couto , Gil Rocha , Henrique Lopes Cardoso



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Author Details

Joana Urbano
  • skeeled, Bascharage, Luxembourg
  • Artificial Intelligence and Computer Science Laboratory (LIACC), Porto, Portugal
Miguel Couto
  • skeeled, Bascharage, Luxembourg
Gil Rocha
  • Faculty of Engineering, University of Porto, Portugal
  • Artificial Intelligence and Computer Science Laboratory (LIACC), Porto, Portugal
Henrique Lopes Cardoso
  • Faculty of Engineering, University of Porto, Portugal
  • Artificial Intelligence and Computer Science Laboratory (LIACC), Porto, Portugal

Acknowledgements

We want to thank Catarina Correia for the valuable contribution she made in the initial phase of this project.

Cite AsGet BibTex

Joana Urbano, Miguel Couto, Gil Rocha, and Henrique Lopes Cardoso. Inconsistency Detection in Job Postings. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 25:1-25:16, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/OASIcs.LDK.2021.25

Abstract

The use of AI in recruitment is growing and there is AI software that reads jobs' descriptions in order to select the best candidates for these jobs. However, it is not uncommon for these descriptions to contain inconsistencies such as contradictions and ambiguities, which confuses job candidates and fools the AI algorithm. In this paper, we present a model based on natural language processing (NLP), machine learning (ML), and rules to detect these inconsistencies in the description of language requirements and to alert the recruiter to them, before the job posting is published. We show that the use of an hybrid model based on ML techniques and a set of domain-specific rules to extract the language details from sentences achieves high performance in the detection of inconsistencies.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Natural language processing
  • Applied computing → Enterprise ontologies, taxonomies and vocabularies
Keywords
  • NLP
  • Ambiguities
  • Contradictions
  • Recruitment software

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