KDD Papers

Supporting Employer Name Normalization at both Entity and Cluster Level

Qiaoling Liu (CareerBuilder LLC);Faizan Javed (Careerbuilder);Vachik Dave (Indiana University - Purdue University, Indianapolis);Ankita Joshi (University of Georgia)


In the recruitment domain, the employer name normalization task, which links employer names in job postings or resumes to entities in an employer knowledge base (KB), is important to many business applications. In previous work, we proposed the CompanyDepot system, which used machine learning techniques to address the problem. After applying it to several applications at CareerBuilder, we faced several new challenges: 1) how to avoid duplicate normalization results when the KB is noisy and contains many duplicate entities; 2) how to address the vocabulary gap between query names and entity names in the KB; and 3) how to use the context available in jobs and resumes to improve normalization quality. To address these challenges, in this paper we extend the previous CompanyDepot system to normalize employer names not only \textit{at entity level}, but also \textit{at cluster level} by mapping a query to a cluster in the KB that best matches the query. We also propose a new metric called \textit{clustering risk} for evaluating the cluster-level normalization. Moreover, we perform query expansion based on five data sources to address the vocabulary gap challenge and leverage the url context for the employer names in many jobs and resumes to improve normalization quality. We show that the proposed CompanyDepot-V2 system outperforms the previous CompanyDepot system and several other baseline systems over multiple real-world datasets. We also demonstrate the large improvement on normalization quality from entity-level to cluster-level normalization.