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Large-Scale Occupational Skills Normalization for Online Recruitment

机译:在线招聘的大规模职业技能标准化

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Job openings often go unfulfilled despite a surfeit of unemployed or underemployed workers. One of the main reasons for this is a mismatch between the skills required by employers and the skills that workers possess. This mismatch, also known as the skills gap, can pose socioeconomic challenges for an economy. A first step in alleviating the skills gap is to accurately detect skills in human capital data such as resumes and job ads. Comprehensive and accurate detection of skills facilitates analysis of labor market dynamics. It also helps bridge the divide between supply and demand of labor by facilitating reskilling and workforce training programs. In this paper, we describe SKILL, a Named Entity Normalization (NEN) system for occupational skills. SKILL is composed of 1) A skills tagger which uses properties of semantic word vectors to recognize and normalize relevant skills, and 2) A skill entity sense disambiguation component which infers the correct meaning of an identified skill. We discuss the technical design and the synergy between data science and engineering that was required to transform the system from a research prototype to a production service that serves customers from across the organization. We also discuss establishing customer feedback loops, and it led to improvements to the system over time. SKILL is currently used by various internal teams at CareerBuilder for big data workforce analytics, semantic search, job matching, and recommendations.
机译:尽管失业或已业推移的工人有了一些人,但职业开放往往不会抵达。这是一个主要原因之一是雇主所需技能与工人拥有的技能之间的不匹配。这种不匹配也被称为技能差距,可以对经济构成社会经济挑战。缓解技能差距的第一步是准确地检测人力资本数据的技能,如恢复和工作广告。全面准确地检测技能促进劳动力市场动态的分析。它还有助于通过促进重塑和劳动力培训计划来弥合劳动力供需之间的划分。在本文中,我们描述了用于职业技能的命名实体归一化(NEN)系统的技能。技能由1)一种技能标记器,它使用语义字向量的属性来识别和正常化相关技能,以及2)技能实体感知歧义组件,其揭示了所识别的技能的正确含义。我们讨论了从研究原型将系统转换为从整个组织提供客户的生产服务所需的数据科学和工程之间的技术设计和协同作用。我们还讨论建立客户反馈循环,并导致随着时间的推移改进系统。目前在大数据劳动力分析,语义搜索,作业匹配和建议中由CareerBuilder使用各种内部团队使用的技能。

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