Machine Learning and NLP Based Resume Parsing Framework For E-Recruitment

Machine Learning and NLP Based Resume Parsing Framework For E-Recruitment

Snehal Nikam, Prathmesh Patil, Supriya Pingale, Yashaswini Rajebhosale, Prof. K. B. Dhomse.

1 nikamsnehal208@gmail.com, Student of BE. Dept. of Information Technology MET-IOE, Nashik, India

2 ap698431@gmail.com, Student of BE. Dept. of Information Technology MET-IOE, Nashik, India

3 supriya.pingale24@gmail.com, Student of BE. Dept. of Information Technology MET-IOE, Nashik, India

4 yashaswinirajebhosle@gmail.com, Student of BE. Dept. of Information Technology MET-IOE, Nashik, India

5Internal Guide Dept. of Information Technology MET’s Institute of Engineering, Nashik, India

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Abstract - Today, there is a vast amount of data being generated. Big data refers to large and complex data sets characterized by high Velocity, Volume, and Variety. Big data encompasses various types of data such as social data, machine data, and transaction-based data. Social data is gathered from platforms like Facebook and Twitter, while machine data includes information from RFID chip readings and GPRS. Transaction-based data consists of data from retail websites. Text data, a significant component of big data, is structured data. Text analytics involves extracting high-quality structured data from unstructured text. One text analytics technique is CV parsing, which involves parsing resumes or CVs. CV parsing integrates a candidate's resume with the recruitment workflow and automatically processes incoming CVs. This paper introduces a CV parser model that utilizes text analytics to extract entities necessary for the recruitment process in companies.

Key Words:  CV, Parser, Big-Data, Text Analytics.