sCooL: A system for academic institution name normalization

Ferosh Jacob, F. Javed, Meng Zhao, M. McNair
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引用次数: 18

Abstract

Named Entity Normalization involves normalizing recognized entities to a concrete, unambiguous real world entity. Within the purview of the online job posting domain, academic institution name normalization provides a beneficial opportunity for CareerBuilder (CB). Accurate and detailed normalization of academic institutions are important to perform sophisticated labor market dynamics analysis. In this paper we present and discuss the design and the implementation of sCooL, an academic institution name normalization system designed to supplant the existing manually maintained mapping system at CB. We also discuss the specific challenges that led to the design of sCooL. sCooL leverages Wikipedia to create academic institution name mappings from a school database which is created from job applicant resumes posted on our website. The mappings created are utilized to build a database which is then used for normalization. sCooL provides the flexibility to integrate mappings collected from different curated and non-curated sources. The system is able to identify malformed data and K-12 schools from universities and colleges. We conduct an extensive comparative evaluation of the semi-automated sCooL system against the existing manual mapping implementation and show that sCooL provides better coverage with improved accuracy.
学校:一种学术机构名称规范化的制度
命名实体规范化涉及将已识别实体规范化为具体的、明确的现实世界实体。在在线招聘领域的范围内,学术机构名称规范化为凯业必达(CB)提供了一个有利的机会。学术机构的准确和详细的规范化对于进行复杂的劳动力市场动态分析至关重要。在本文中,我们提出并讨论了学校的设计和实现,这是一个学术机构名称规范化系统,旨在取代CB现有的手动维护的映射系统。我们还讨论了导致学校设计的具体挑战。学校利用维基百科从学校数据库中创建学术机构名称映射,该数据库是根据我们网站上发布的求职者简历创建的。创建的映射用于构建数据库,然后用于规范化。学校提供了集成从不同的策划和非策划来源收集的映射的灵活性。该系统能够识别大学和学院的畸形数据和K-12学校。我们对半自动化的学校系统与现有的手工制图实施进行了广泛的比较评估,并表明学校提供了更好的覆盖范围和更高的准确性。
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