SCooL: A system for academic institution name normalization
|SCooL: A system for academic institution name normalization|
|Author(s)||Jacob F., Javed F., Zhao M., McNair M.|
|Published in||2014 International Conference on Collaboration Technologies and Systems, CTS 2014|
|Keyword(s)||Lucene, Name Entity Recognition, School Normalization, Wikipedia (Extra: Societies and institutions, Academic institutions, Comparative evaluations, Lucene, Name entity recognition, Named entity normalizations, School Normalization, Universities and colleges, Wikipedia, Mapping)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of conference papers|
SCooL: A system for academic institution name normalization is a 2014 conference paper written in English by Jacob F., Javed F., Zhao M., McNair M. and published in 2014 International Conference on Collaboration Technologies and Systems, CTS 2014.
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.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers.