Named Entity Recognition Driven Synthesis of IT Job Descriptions in Morocco: A Comparative Analysis of BERT and BiLSTM Models
Keywords:
BERT, BiLSTM, Information Technology, Job descriptions, Named Entity Recognition, Summarization.Abstract
The information technology (IT) sector, characterized by its dynamism and diversity, represents a major challenge for jobseekers and recruiters alike, who have to navigate through massive lists of job offers to extract relevant information. This article proposes a new approach to meeting this challenge by integrating Named Entity Recognition (NER) into the synthesis of job descriptions in the IT domain. This exploration in the IT sector offers a significant contribution to the optimization of job search processes and recruitment strategies specific to this sector. Our approach, which includes the conceptualization, data preparation and training of BERT (Bidirectional Encoder Representations from Transformers) and BiLSTM (Bi-directional Long Short-Term Memory) models, enables us to compare the performance of two NER models through in-depth evaluation. The originality of our approach lies in the use of Named Entity Recognition (NER) as the cornerstone of automatic synthesis. By harnessing the power of NER, we simplify and streamline the process of efficiently extracting crucial information such as organizations, locations and job titles. The results underline the transformative potential of NER in improving the accessibility and comprehensibility of complex information contained in job advertisements in the IT sector. By automating the extraction of relevant entities such as job titles, skills required, company names, work locations, responsibilities requested, technical and non-technical skills, diplomas and years of experience required, we facilitate the job search process. Our evaluations show that BERT models outperform BiILSTM models in terms of accuracy and performance in named entity recognition, demonstrating their superiority for this specific task.
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