Technical Skills Information Extraction From Resumes Using Advanced Natural Language Processing Model With Transfer Learning

Matching job seekers with suitable job vacancies in the rapidly evolving job market is challenging. Enhancing the accuracy of job matching requires identifying specific skills and techniques essential for each vacancy. This study proposes an innovative algorithm that utilizes Transfer Learning combined with advanced Natural Language Processing (NLP) models specifically SpaCy, BERT (Bidirectional Encoder Representations from Transformers), and XLNet to automatically detect and extract skills and techniques from job vacancy descriptions. The algorithm initiates by pre-processing job descriptions through tokenization and text cleaning. Next, SpaCy is used for part-of-speech tagging, entity recognition, and syntactic parsing to extract relevant keywords and phrases. These pre-processing steps effectively reduce noise and improve the quality of extracted data. BERT and XLNet, two cutting-edge transformer-based models, are then fine-tuned on a large corpus of job description data. This fine-tuning adapts the models to the specific language and context of job descriptions, enabling them to encode reprocessed descriptions into contextual embeddings. At the algorithm core is the identification of skills and techniques within job descriptions by leveraging these contextual embeddings. A combination of named entity recognition, keyword extraction, and context-based keyword filtering is employed to identify essential skills and techniques for each role. The algorithm’s performance is evaluated using precision, recall, and F1-score metrics on labeled job description datasets. Results show the algorithm effectiveness in detecting skills and techniques, providing significant benefits in automating and enhancing the job matching process. By accurately identifying required skills, job seekers can connect with more relevant opportunities, and employers can streamline recruitment. This approach has the potential to improve job market efficiency, leading to more successful placements.