职位描述中经验等级分类的建模方法评估
Evaluating Modeling Approaches for Experience-Level Classification in Job Description
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中文总结 AI 辅助
本文提出结构感知的Section-Aware BERT方法，利用招聘文本的段落结构提升职位经验等级分类性能，并通过与大型语言模型的对比及错误分析揭示了任务挑战与有效建模路径。
中文摘要 AI 辅助
本文研究了招聘文本中预测职位经验等级的任务，旨在自动识别职位所需的资格要求。与传统文本分类不同，招聘文本通常具有明确的内部结构，不同段落对传达经验线索的作用不成比例。为解决这一问题，我们提出了一种结构感知的Section-Aware BERT方法，该方法在基于规则系统和经典基线TF-IDF的基础上，对关键段落（职位标题、职责、要求）进行分段和编码，以进行集成建模。同时，我们在同一数据集上评估了大型语言模型在少样本和微调设置下的表现，以比较不同建模范式的能力边界。实验结果表明，显式利用文本结构显著提高了经验等级预测性能，尤其是在职位标题模糊的场景中。进一步的错误分析揭示了该任务中的系统性挑战，包括入门级与高级之间的混淆以及中级职位边界的模糊性。本研究为理解结构化招聘文本提供了一种有效的建模方法和分析框架。
英文摘要
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 settings on the same dataset to compare the capability boundaries of different modeling paradigms. Experimental results demonstrate that explicitly leveraging text structure significantly improves experience level prediction performance, particularly in scenarios with ambiguous job titles. Further error analysis reveals systemic challenges in this task, including confusion between Entry and Senior levels and the blurred boundaries of Mid-level positions. This research provides an effective modeling approach and analytical framework for understanding structured recruitment texts.
发表机构
- New York University（纽约大学）
机构由 AI 辅助整理，请以论文原文为准。
