使用命名实体识别和关系提取管道生成动态分类系统,用于未来的技能识别.
Luis Jose Gonzalez-Gomez1, Sofia Margarita Hernandez-Munoz2, Abiel Borja2
1Institute for the Future of Education, Tecnologico de Monterrey, Monterrey, Mexico.
Frontiers in artificial intelligence
|July 17, 2025
概括
本研究介绍了一种使用自然语言处理 (NLP) 来识别和预测未来技能的动态分类,以应对不断变化的劳动力市场. 该系统有助于弥合当前和未来劳动力需求之间的差距,以便更好地规划.
科学领域:
- 计算语言学 计算语言学
- 劳动经济学 劳动经济学
- 信息科学 信息科学 信息科学
背景情况:
- 劳动力市场正在迅速发展,在当前的知识,技能和能力 (KSA) 和未来的职业需求之间造成了差距.
- 世界经济论坛和经合组织等组织强调了动态技能识别的必要性,以适应这些变化.
研究的目的:
- 开发一种用于构建技能动态分类的新系统.
- 利用自然语言处理 (NLP) 技术,包括命名实体识别 (NER) 和关系提取 (RE),用于识别和预测未来的技能.
- 弥合当前劳动力能力与未来需求之间的差距,支持教育和专业发展.
主要方法:
- 开发了一个基于NLP的架构,结合了文本预处理,NER和RE模型.
- 从劳动力市场报告中,NER模型识别和分类了KSA和职业.
- RE模型建立了识别实体之间的语义关系,训练了超过1700份注释文件.
主要成果:
- 该NER模型在实体识别方面获得了65.38%的微平均F1分数.
- 该RE模型在关系分类方面获得了82.2%的微F1评分.
- 生成的分类学成功地识别了新兴的技能和职业,证明了对劳动力市场转变的动态适应能力.
结论:
- 动态分类学提供实时更新的能力,并预测新兴技能趋势,作为一个有价值的劳动力规划工具.
- 虽然NER显示出强烈的召回,但需要改进精度;未来的工作将扩展集体并改进模型.
- 该系统提供了对未来劳动力需求的见解,可以在多个部门应用.
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