用大模型提升岗位匹配，链接欧洲职业与资质体系
Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies
- 采用句级与实体级链接方法，融合语言模型增强职位分类
- 构建两个标注数据集，验证职业与资质在招聘文本中的表达方式
- 开源工具支持劳动力市场研究，适合政策与就业平台开发者
本研究探讨语言模型在提升劳动力市场信息分类方面的潜力，通过将职位空缺文本与欧洲技能、能力、资格和职业（ESCO）分类体系及欧洲资格框架（EQF）进行关联。比较了文献中两种主流方法：句子链接与实体链接。为支持持续研究，我们发布一个开源工具，集成这两种方法，助力劳动力分类与就业话语分析。为突破表面技能提取，构建了两个专门用于评估职位文本中职业与资质表现的标注数据集。此外，还探索了生成式大模型在此任务中的多种应用方式。研究结果推动了职位实体抽取的技术前沿，并提供了数字化经济下工作、技能与劳动力叙事的计算基础设施。代码已公开：https://github.com/tabiya-tech/tabiya-livelihoods-classifier
原文摘要 · Abstract (English)
This study investigates the potential of language models to improve the classification of labor market information by linking job vacancy texts to two major European frameworks: the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and the European Qualifications Framework (EQF). We examine and compare two prominent methodologies from the literature: Sentence Linking and Entity Linking. In support of ongoing research, we release an open-source tool, incorporating these two methodologies, designed to facilitate further work on labor classification and employment discourse. To move beyond surface-level skill extraction, we introduce two annotated datasets specifically aimed at evaluating how occupations and qualifications are represented within job vacancy texts. Additionally, we examine different ways to utilize generative large language models for this task. Our findings contribute to advancing the state of the art in job entity extraction and offer computational infrastructure for examining work, skills, and labor market narratives in a digitally mediated economy. Our code is made publicly available: https://github.com/tabiya-tech/tabiya-livelihoods-classifier
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