SALARY PREDICTION FROM JOB DESCRIPTIONS USING ATTENTION-BASED NLP MODELS

ЖҰМЫС СИПАТТАМАЛАРЫ НЕГІЗІНДЕ НАЗАР АУДАРУ ӘДІСІН ҚОЛДАНАТЫН NLP МОДЕЛЬДЕРІМЕН ЖАЛАҚЫНЫ БОЛЖАУ
ПРОГНОЗИРОВАНИЕ ЗАРАБОТНОЙ ПЛАТЫ ПО ОПИСАНИЯМ ВАКАНСИЙ С ИСПОЛЬЗОВАНИЕМ NLP-МОДЕЛЕЙ НА ОСНОВЕ МЕХАНИЗМА ВНИМАНИЯ
Ashim Z. Botanov A. Abdoldina F. Serek A.
2025 Kazakh-British Technical University

Herald of the Kazakh British Technical UNiversity
2025 #22 Issue 4 168 - 177 pp.

The research introduces a dual deep learning system which predicts salary ranges by processing job descriptions through BERT-based contextual embeddings and structured metadata integration. The proposed method utilizes more than 124,000 LinkedIn job postings to merge BERT-based contextual embeddings with structured information about location and industry and experience level and compensation type. The model uses multi-head attention to identify essential salary-related terms in job descriptions which results in better model interpretability and improved prediction accuracy. The model combines semantic embeddings with tabular data to create a multimodal representation which serves as input for supervised learning with an ordinal-aware loss function. The model achieves stable performance in salary classification across three categories through F1-scores between 0.82 and 0.84. The proposed model achieves excellent generalization capabilities for different sectors and job types while providing precise predictions and clear decision-making processes for salary benchmarking and recruitment analytics applications.

Attention Mechanism , BERT Embeddings , Job Descriptions , Natural Language Processing (NLP) , Salary Prediction

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Kazakh-British Technical University, Almaty, Kazakhstan
Institute of Automatics and Information Technologies, Satbayev University, Almaty, Kazakhstan
Astana IT University, Astana, Kazakhstan

Kazakh-British Technical University
Institute of Automatics and Information Technologies
Astana IT University

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