Information Extraction from CV Documents Using a Hybrid Natural Language Processing and Rule-Based Approach
Ekstraksi Informasi dari Dokumen CV Menggunakan Pendekatan Hybrid Natural Language Processing dan Rule-Based
DOI:
https://doi.org/10.21070/ups.9859Keywords:
Natural Language Processing, Applicant Tracking System, Information Extraction, Resume Parsing, Hybrid Approach, Rule-Based SystemAbstract
Digital transformation in recruitment mandates efficient Applicant Tracking Systems (ATS), yet variable, unstructured CV formats remain a primary obstacle. Traditional rule-based methods often lack flexibility, while Deep Learning models incur high computational costs. This study presents a hybrid CV information extraction system designed to balance accuracy and efficiency.
The methodology integrates Rule-Based algorithms (Regular Expressions) for factual data with Natural Language Processing (NLP)—specifically the spaCy library—for analyzing competencies and experience. Empirical results demonstrate robust performance, achieving a Precision of 91.3%, Recall of 87.5%, and an F1-Score of 89.4%. The system attained an overall accuracy of 80.8% with a processing time of under 2 seconds per document, proving that hybrid methods effectively manage CV complexity without sacrificing computational speed.
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