RINX: A system for information and knowledge extraction from resumes

Abstract

A resume is a detailed source of information about the candidate which summarizes the personal details, education, career history, project experience, certifications, trainings, awards, and any other achievements. For large organizations or job portals which receive thousands of resumes for recruitment or profile creation, it is not possible to manually go through each resume and identify the important information. Hence, there is a need for a system that automatically extracts the information of interest from the resumes. Such automatic extraction of information from resumes is very challenging because resumes are unstructured documents with a wide range of variations in terms of format, style, and contents. In this paper, we describe RINX (Resume INformation eXtraction) which is an end-to-end system for automatic extraction of information from resumes. RINX heavily utilizes traditional approaches like linguistic patterns and gazettes for information extraction. RINX also complements these traditional approaches with state-of-the-art machine learning and deep learning based techniques. We further describe a few knowledge extraction techniques as well as several real-life use-cases based on the information extracted from a large repository of resumes.