电子招聘系统的分析与不足：基于语义的方法解决知识不完整性和有限的领域覆盖
Analysis & Shortcomings of E-Recruitment Systems: Towards a Semantics-based Approach Addressing Knowledge Incompleteness and Limited Domain Coverage
摘要：
因特网的迅速发展导致采用新的电子征聘和人力资源管理方法。这些方法旨在通过结合自然语言处理工具和基于语义的方法，系统地解决传统招聘程序的局限性。在这种情况下，对于给定的工作职位，申请人简历（通常作为不同格式的自由文本非结构化文档上传，诸如.pdf、.doc或.rtf）使用由诸如职业类别和基于语义的技术的附加资源丰富的传统的基于关键字的模型来匹配/筛选。事实证明，采用这些技术可以有效地减少传统招聘和候选人选择方法所需的成本、时间和精力。然而，技能差距，即倾向于精确地检测和提取申请人简历和职位中的相关技能，以及申请人简历中隐藏的语义维度仍然是电子招聘系统的主要障碍。这是因为目前的电子征聘系统所利用的资源是从通用的独立于领域的来源获得的，因此造成知识不完整和缺乏领域覆盖。在本文中，我们回顾了国家的最先进的电子招聘方法，并强调在这一领域的最新进展。电子招聘框架，解决目前的缺点，通过使用多个合作的语义资源，特征提取技术和技能相关性措施进行了详细说明。提出了一个实例化的建议框架和实验验证，使用现实世界的招聘数据集从两个就业门户网站证明了所提出的方法的有效性。
英文摘要：
The rapid development of the Internet has led to introducing new methods for e-recruitment and human resources management. These methods aim to systematically address the limitations of conventional recruitment procedures through incorporating natural language processing tools and semantics-based methods. In this context, for a given job post, applicant resumes (usually uploaded as free-text unstructured documents in different formats such as .pdf, .doc, or .rtf) are matched/screened out using the conventional keyword-based model enriched by additional resources such as occupational categories and semantics-based techniques. Employing these techniques has proved to be effective in reducing the cost, time, and efforts required in traditional recruitment and candidate selection methods. However, the skill gap, i.e. the propensity to precisely detect and extract relevant skills in applicant resumes and job posts, and the hidden semantic dimensions encoded in applicant resumes still form a major obstacle for e-recruitment systems. This is due to the fact that resources exploited by current e-recruitment systems are obtained from generic domain-independent sources, therefore resulting in knowledge incompleteness and the lack of domain coverage. In this paper, we review state-of-the-art e-recruitment approaches and highlight recent advancements in this domain. An e-recruitment framework addressing current shortcomings through the use of multiple cooperative semantic resources, feature extraction techniques and skill relatedness measures is detailed. An instantiation of the proposed framework is proposed and an experimental validation using a real-world recruitment dataset from two employment portals demonstrates the effectiveness of the proposed approach.
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。
