NDLI: Supporting Employer Name Normalization at both Entity and Cluster Level

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Supporting Employer Name Normalization at both Entity and Cluster Level

Content Provider	ACM Digital Library
Author	Dave, Vachik S. Liu, Qiaoling Javed, Faizan Joshi, Ankita
Abstract	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 at entity level, but also at cluster level by mapping a query to a cluster in the KB that best matches the query. We also propose a new metric based on success rate and diversity reduction ratio 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.
Starting Page	1883
Ending Page	1892
Page Count	10
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098093
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Entity linking Success rate Cluster-level normalization Diversity reduction ratio Employer name normalization
Content Type	Text
Resource Type	Article

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