Martseniuk, K.Deveciogullari, A.2024-11-122024-11-122024Martseniuk, K. Evaluation of unstructured resumes using the Word2Vec model / K. Martseniuk, A. Deveciogullari // Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2024. – № 2 (45). – С. 134-142. – Бібліогр.: 8 назв.https://ela.kpi.ua/handle/123456789/70531The article addresses the problem of evaluating candidates' resumes for job vacancies using various natural language processing (NLP) methods. Traditional text processing algorithms were analyzed, and a critical drawback of these methods was identified—their inability to account for semantic relationships between words, which is particularly important in the context of resume evaluation. The BERT model was also considered, but it was dismissed due to its high computational complexity and excessive functionality for this task. The primary choice for evaluating resume relevance was the Word2Vec method, which accounts for semantic relationships between words, contributing to greater objectivity and accuracy in the evaluation process. The study results confirm the effectiveness of using Word2Vec compared to other methods in the context of resume analysis.enmachine learningresume evaluationnatural language processingWord2Vecsynonyms and related wordscosine similarityArticleС. 134-142004.043