Resume Parsing using Natural Language Processing

Screening resumes out of bulk is a challenging task, and recruiters or hiring managers waste a lot of their valuable time searching through each resume. Job seekers should have access to the best tools to find the perfect match for their profile without wasting time on irrelevant recommendations and manual searches. Often resumes are populated with irrelevant and unnecessary information. Therefore, parsing thousands of resumes manually consumes a lot of time and energy; thereby, it makes the hiring process expensive. In general, however, traditional job recommendation systems are based on simple keyword and semantic similarities that are usually not well suited to providing good job recommendations since they don’t consider the interlinks between entities. In this paper, screening resumes is automated by using advanced Natural Language. Our model helps the recruiters to screen the resumes based on job descriptions within no time. It makes the hiring process easy and efficient by extracting the required entities automatically by using the Spacy NER model from the resumes and Joint NER and relation extraction will offer an entirely new approach to retrieving information using knowledge graphs, where you can explore different nodes to find hidden relationships.