| Yazarlar (4) |
Senem Tanberk
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Selahattin Serdar Helli
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Ege Kesim
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Sena Nur Cavsak
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| Özet |
| Online job search through websites has been a remarkably advantageous tool for both job seekers and employers, effectively serving their needs for numerous years. The job-resume matching system uses natural language processing (NLP) techniques to analyze the content of resumes and job descriptions. In this study, we used AI models to analyze resumes and rank each resume based on its similarity to the job description. This proposed framework uses optical character recognition and object recognition models to detect text and sections from resumes. Afterward, the text preprocessing step is applied. The preprocessed section is classified with the BERT model. The section of resumes classified into 5 different categories (education, experience, skills, personal, and language) are sent to the information extraction module. By using the named entity recognition method, the module identifies 8 different … |
| Anahtar Kelimeler |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | 2023 8th International Conference on Computer Science and Engineering (UBMK) |
| Kongre Tarihi | 13-09-2023 / 13-09-2023 |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir |
| Atıf Sayıları | |
| Google Scholar | 6 |
