Online job ads serve as a valuable source of information for skill
requirements, playing a crucial role in labor market analysis and e-recruitment
processes. Since such ads are typically formatted in free text, natural
language processing (NLP) technologies are required to automatically process
them. We specifically focus on the task of detecting skills (mentioned
literally, or implicitly described) and linking them to a large skill ontology,
making it a challenging case of extreme multi-label classification (XMLC).
Given that there is no sizable labeled (training) dataset are available for
this specific XMLC task, we propose techniques to leverage general Large
Language Models (LLMs). We describe a cost-effective approach to generate an
accurate, fully synthetic labeled dataset for skill extraction, and present a
contrastive learning strategy that proves effective in the task. Our results
across three skill extraction benchmarks show a consistent increase of between
15 to 25 percentage points in \textit{R-Precision@5} compared to previously
published results that relied solely on distant supervision through literal
matches.

通过使用大型语言模型，本研究提出了一种成本有效的方法来生成准确、全合成的技能提取的标签数据集，并提出了一种对比学习策略，证明其在任务中的有效性。在三个技能提取基准测试中，我们的结果显示，与仅依赖于远程监督的字面匹配的先前发布结果相比，R-Precision@5 提高了 15 至 25 个百分点。