CVs Classification Using Neural Network Approaches Combined with BERT and Gensim: CVs of Moroccan Engineering Students

Abstract

Deep learning (DL)-oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpus of data. The article examines the application of deep learning approaches, based on different neural network methods, including Gated Recurrent Unit (GRU), long short-term memory (LSTM), and convolutional neural networks (CNNs). The compared models were combined with two different word embedding techniques, namely: Bidirectional Encoder Representations from Transformers (BERT) and Gensim Word2Vec. The models are designed to evaluate the performance of architectures based on neural network techniques for the classification of CVs of Moroccan engineering students at ENSAK (National School of Applied Sciences of Kenitra, Ibn Tofail University). The used dataset included CVs collected from engineering students at ENSAK in 2023 for a project on the employability of Moroccan engineers in which new approaches were applied, especially machine learning, deep learning, and big data. Accordingly, 867 resumes were collected from five specialties of study (Electrical Engineering (ELE), Networks and Systems Telecommunications (NST), Computer Engineering (CE), Automotive Mechatronics Engineering (AutoMec), Industrial Engineering (Indus)). The results showed that the proposed models based on the BERT embedding approach had more accuracy compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/BERT model achieved slightly better accuracy with 0.9351 compared to other hybrid models. On the other hand, single learning models also have good metrics, especially based on BERT embedding architectures, where CNN has the best accuracy with 0.9188.