HR Tech Analyst: Automated Resume Parsing and Ranking System through Natural Language Processing

B Nisha · V. Manobharathi · B Jeyarajanandhini · G. Sivakamasundari

18 citations0/0 files storedInternational Conference on Automation, Computing and Renewable Systems (ICACRS)

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AI and HR Technologies Employer Branding and e-HRM Advanced Text Analysis Techniques

Abstract

The rapid evolution of technology has prompted organizations to adopt innovative solutions to streamline their recruitment processes. It presents an Automated Resume Parsing and Ranking System (ARRS) powered by Natural Language Processing (NLP) techniques. The primary goal of ARRS is to enhance the efficiency and effectiveness of the talent acquisition process. ARRS leverages NLP algorithms to extract valuable information from candidate resumes, such as skills, qualifications, and work experience. These extracted data points are then used to rank candidates based on predefined criteria, effectively eliminating the manual and time-consuming screening process. Moreover, ARRS can be customized to align with specific job requirements and organizational preferences, making it a versatile tool for various industries and roles. In addition to its parsing capabilities, ARRS offers robust candidate ranking functionality. By considering multiple factors such as skill match, experience level, and education, the system provides recruiters and hiring managers with a prioritized list of candidates, allowing them to focus their attention on the most qualified individuals. ARRS is designed with user-friendliness in mind, featuring an intuitive interface that simplifies the recruitment workflow. It integrates seamlessly with existing Applicant Tracking Systems (ATS) and provides real-time insights into the recruitment pipeline, enabling data-driven decision-making.

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References

2020Facial Emotion Recognition Using Deep Convolutional Neural Network 2023Resumate: A Prototype to Enhance Recruitment Process with NLP based Resume Parsing 2022Resume Parsing Framework for E-recruitment 2023Improved Resume Parsing based on Contextual Meaning Extraction using BERT 2020NLP based Extraction of Relevant Resume using Machine Learning 2021Resume Validation and Filtration using Natural Language Processing 2021Personality Prediction Through CV Analysis using Machine Learning Algorithms for Automated E-Recruitment Process 2020Automatic Extraction of Segments from Resumes using Machine Learning

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2024Automated Resume Parsing and Ranking using Natural Language Processing

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