A system for detecting professional skills from resumes written in natural language

• Published in: 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP) pp. 189 - 196
• Main Authors: Chifu, Emil St, Chifu, Viorica Rozina, Popa, Iulia, Salomie, Ioan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2017
• Subjects: Data mining; Hidden Markov models; Natural languages; Ontologies; Resumes; Training
• DOI: 10.1109/ICCP.2017.8117003

In this paper, we present a new method for detecting professional skills (as noun phrases) from resumes written in natural language. The proposed method uses an ontology of skills, the Wikipedia encyclopedia, and a set of standard multi word part-of-speech patterns in order to detect the professional skills. First, the method checks to see if there are, in the text of the resumes, skills that are concepts in our ontology. The method also tries to identify possible new skills, which are not present in our ontology. This is done with the help of some specific, lexicalized, multi-word expression patterns (i.e. specific contexts) that could surround new, unknown skills. The specific expression patterns (specific contexts) are induced by training from a corpus of resumes. This induction of the possible specific contexts for new skills is based on a set of standard, generic part-of-speech patterns (found by hand) that usually contain the skills already present in the ontology. Hence our skill extraction method is based on a bootstrapping approach. The newly detected skills are validated by a human expert and then inserted automatically into the skill ontology. Populating the ontology with the new skills is performed with the help of the Wikipedia encyclopedia. The method proposed has been tested on a set of resumes written by users as well as on a corpus collected by automatically extracting resumes from specific Web sites.