论文标题
利用空缺标题的固有层次结构进行自动化本体扩展
Leveraging the Inherent Hierarchy of Vacancy Titles for Automated Job Ontology Expansion
论文作者
论文摘要
机器学习在在线招聘中扮演着重要角色，为世界上许多最大的就业平台提供了智能的对决和工作建议。但是，主要文本很少足以充分理解职位发布：通常，许多必需的信息都被凝结成工作标题。已经做出了一些有组织的努力，以将职位映射到手工制作的知识库中以提供此信息，但是这些信息仅涵盖了约60％的在线空缺。我们介绍了一种新颖的，纯粹的数据驱动方法来检测新的工作头衔。我们的方法在概念上是简单，极其高效且具有基于NER的方法的竞争力。尽管我们方法的独立应用并不能胜过填充的BERT模型，但它也可以作为预处理步骤也可以应用，从而在几个架构中大大提高了精度。
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.
