利用职位名称的内在层次结构实现职位本体的自动扩展
Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion
摘要：
机器学习在在线招聘中发挥着越来越大的作用，为全球许多最大的就业平台提供智能配对和职位推荐。然而，正文很少足以完全理解一个招聘信息：通常情况下，大部分所需信息都浓缩在职位名称中。已经做出了一些有组织的努力，将职位名称映射到手工制作的知识库中，以提供这一信息，但这些仅覆盖了大约60%的在线空缺。我们引入了一种新的，纯粹的数据驱动的方法来检测新的职位。我们的方法在概念上是简单的，非常有效的和传统的基于NER的方法的竞争力。虽然我们的方法的独立应用并不优于微调的BERT模型，但它也可以作为预处理步骤应用，从而大大提高了几种架构的准确性。
英文摘要：
Machine learning plays an ever-bigger part in online recruitment, powering intelligent matchmaking and job recommendations across many of the world's largest job platforms. However, the main text is rarely enough to fully understand a job posting: more often than not, much of the required information is condensed into the job title. Several organised efforts have been made to map job titles onto a hand-made knowledge base as to provide this information, but these only cover around 60\% of online vacancies. We introduce a novel, purely data-driven approach towards the detection of new job titles. Our method is conceptually simple, extremely efficient and competitive with traditional NER-based approaches. Although the standalone application of our method does not outperform a finetuned BERT model, it can be applied as a preprocessing step as well, substantially boosting accuracy across several architectures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
