Evaluating Modeling Approaches for Experience-Level Classification in Job Description
This paper investigates the task of predicting job experience levels in recruitment texts, aiming to automatically identify the qualifications required for positions. Unlike traditional text classification, recruitment texts typically possess explicit internal structures, with different paragraphs playing disproportionate roles in conveying experience clues. To address this, we propose a structure-aware Section-Aware BERT approach that segments and encodes key paragraphs (titles, responsibilities, requirements) for integrated modeling, building upon rule-based systems and classical baselines TF-IDF. Simultaneously, we evaluate large language models under both few-shot and fine-tuning sett...
| Authors | Celia Liang, Eddie Wu, Shiqi Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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