An Intelligent framework for E-Recruitment System Based on Text Categorization and Semantic Analysis

Abstract

In the field of online job recruiting, accurate job and resume categorization is critical for both the seeker and the recruiter. Using Natural Language Processing (NLP) technology we have developed an autonomous text classification system that POS tag, tokenizes, Lemmatize the data. We have utilized Phrase Matcher to calculate the score of resumes based on recruiter's information, suggest lacking skills to users, and provide the top resumes to the recruiter. Finally, the proposed system is presented together with its findings and analysis. We divided candidates into groups based on the information in their resumes. We used domain adaptation due to the sensitive nature of the resumes content. A Word Order Similarity between Sentences is used to categorize the resume data on large dataset of job description. The System is evaluated and resulted in improved precision and recall.