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ABSTRACT
Job Requirement is considered one of the major activities for humans which is a very strenuous job to find a fruitful talent. Our proposed model is basically to extract the details and statistics from the resume and ranking the resume based on the preference of the company associated and its requirements using the Natural Language Processing (NLP) techniques. Parsing and ranking the resume makes the hiring process easy and efficient. A resume contains various minute data within it and any respectable parser needs to extract out these data such as education, experience, project, address etc. So, basically we are going to build a job portal where the employees and applicants would upload their resume for any particular job and using the NLP technique, the necessary information will be parsed and a structured resume with information will be generated and also the resumes of employee will be ranked according to the requirement of the company skill set and employees skills in the provided resume.
INTRODUCTION
Corporate companies and recruitment agencies process numerous resumes daily. This is no task for
humans. An automated intelligent system is required which can take out all the vital information from the
unstructured resumes and transform all of them to a common structured format which can then be ranked for a
specific job position. Parsed information include name, email address, social profiles, personal websites, years
of work experience, work experiences, years of education, education experiences, publications, certifications,
volunteer experiences, keywords and finally the cluster of the resume (ex: computer science, human resource,
etc.). The parsed information is then stored in a database (NoSQL in this case) for later use. Unlike other
unstructured data (ex: email body, web page contents, etc.), resumes are a bit structured. Information is stored
in discrete sets. Each set contains data about the person’s contact, work experience or education details. In spite
of this, resumes are difficult to parse. This is because they vary in types of information, their order, writing
style, etc. Moreover, they can be written in various formats. Some of the common ones include ‘.txt’, ‘.pdf’,
‘.doc’, ‘.docx’, ‘.odt’, ‘.rtf’ etc. To parse the data from different kinds of resumes effectively and efficiently, the
model must not rely on the order or type of data.
