An Approach Based on Named Entity Recognition and Semantic Analysis for Recruitment Efficiency and Optimization

Modern recruitment requires smarter, faster, and more inclusive methods to manage the growing volume and diversity of job applications and candidate resumes. Manual screening is often ineffective and unreliable especially in low-resource or multilingual contexts. To address this challenge, we propose an approach that automates and optimizes key stages of the recruitment process. This three-stage approach includes: 1) extracting structured data from resumes using a robust Named Entity Recognition (NER) system, which comprises a NER annotator, a feature extractor, and a transition-based parser; 2) employing a fine-tuned transformer model to perform semantic matching between candidates and job descriptions; and 3) leveraging a large language model to generate interview questions tailored to specific job requirements, thereby improving the relevance and personalization of candidate assessments. The recruitment system was tested on a large-scale resume and job posting dataset across multiple domains. Our NER model reported an F1-score of 85.11% in entity extraction, and the matching component reported accuracy levels as high as 92%when using hierarchical job classes. The results prove the efficacy of combining deep learning techniques with semantic reasoning in enhancing automation, accuracy, and fairness in hiring.

@article{Ifakir2025,
  title     = {An Approach Based on Named Entity Recognition and Semantic Analysis for Recruitment Efficiency and Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ismail Ifakir and Noureddine Mohtaram and El Habib Nfaoui and Abderrahim Zannou and Mohammed El Hassouni},
  doi       = {10.14569/IJACSA.2025.0160889},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160889}
}