Resume Parsing System using NLP and Semi-Structured Supervised Networks

Abstract

This paper presents a novel approach to automating the extraction and structuring of resume information by leveraging cutting-edge techniques in Natural Language Processing (NLP) and a semi-structured Bidirectional Encoder Representations from Transformers (BERT) Network. Traditionally, manual resume parsing is time-consuming and prone to human errors, which can significantly reduce the efficiency and effectiveness of recruitment processes, talent acquisition, and overall human resource management. By incorporating advanced NLP methods with a semi-supervised BERT framework, our solution addresses these challenges, offering the ability to process labeled and unlabeled datasets with improved accuracy and scalability. The system architecture integrates sentence tokenization, BILOU tagging, and other pre-processing techniques to ensure optimal data preparation before model training. With a training accuracy of 83% and moderate-to-high precision across a range of entity tags such as skills, experience, and education, this method demonstrates the potential for revolutionizing the resume parsing landscape. The combined power of NLP and deep learning techniques leads to a more robust, scalable, and reliable solution.