An Intelligent Text Mining Framework for Decision Support in Government Hiring

Abstract

Government hiring processes face significant challenges in efficiently and equitably matching qualified candidates with appropriate positions while managing high application volumes. This paper presents an Intelligent Text Mining Framework for decision support in government hiring that integrates lexical and semantic similarity pathways through a weighted fusion mechanism. The framework processes resume and job description corpora using parallel TF-IDF (lexical) and Sentence-BERT (semantic) analyses, combines scores via a tunable parameter α, and ranks candidates per job description. A comprehensive evaluation on a dataset of 100 resumes and 10 job descriptions demonstrates that the hybrid approach achieves a mean combined similarity score of 0.642 ± 0.113 with high reliability (split half correlation r=0.891, p<0.001). The automated pipeline reduces screening time by 99.97% compared to manual review, reclaiming approximately 5.2 person months of effort per 1,000 comparisons. Using non-sensitive proxy variables like resume length and professional category, rigorous fairness tests show no disproportionate impact (80% rule ratio = 0.858) and no statistically significant bias between groups (Kruskal Wallis p=0.543). The system contains an AI dashboard that shows how scores are spread out, how the best candidates rank, and how big the skill gaps are. This helps hiring supervisors keep track of what's going on. The results suggest that the dual path method is a solid balance between precision and recall, helps choose candidates equitably, and is a scalable, auditable way to hire people in the public sector. This study provides a proven, open-source technology that improves government recruiting by making it more efficient, fair, and open, while still allowing for human monitoring and following ethical hiring norms.