Version 1
: Received: 3 March 2024 / Approved: 3 March 2024 / Online: 4 March 2024 (09:44:13 CET)
How to cite:
Qostal, A.; Moumen, A.; Lakhrissi, Y. Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students. Preprints2024, 2024030111. https://doi.org/10.20944/preprints202403.0111.v1
Qostal, A.; Moumen, A.; Lakhrissi, Y. Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students. Preprints 2024, 2024030111. https://doi.org/10.20944/preprints202403.0111.v1
Qostal, A.; Moumen, A.; Lakhrissi, Y. Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students. Preprints2024, 2024030111. https://doi.org/10.20944/preprints202403.0111.v1
APA Style
Qostal, A., Moumen, A., & Lakhrissi, Y. (2024). Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students. Preprints. https://doi.org/10.20944/preprints202403.0111.v1
Chicago/Turabian Style
Qostal, A., Aniss Moumen and Younes Lakhrissi. 2024 "Resumes Classification Using Neural Network Approaches Combined with Bert and Gensim: CVS of Moroccan Engineering Students" Preprints. https://doi.org/10.20944/preprints202403.0111.v1
Abstract
Deep Learning (DL) oriented document processing is widely used in different fields for extraction, recognition, and classification processes from raw corpora 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 (CNN). 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 resumes 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, Networks and Systems Telecommunications, Computer Engineering, Automotive Mechatronics Engineering, Industrial Engineering). The results revealed good performance of the proposed models based on the BERT embedding approach compared to models based on the Gensim Word2Vec embedding approach. Accordingly, the CNN-GRU/Bert model achieved slightly better accuracy with 0.9251 compared to other hybrid models.
Keywords
Gated Recurrent Unit (GRU); Long Short-Term Memory (LSTM); Convolutional Neural Networks (CNN); BERT; Gensim; Moroccan engineering students; Ibn Tofail University; Resumes; CVs; ENSAK
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
