Matt McNair

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Named Entity Recognition (NER) and Named Entity Normalization (NEN) refer to the recognition and normalization of raw texts to known entities. From the perspective of recruitment innovation, professional skill characterization and normalization render human capital data more meaningful both commercially and socially. Accurate and detailed normalization of(More)
In the online job recruitment domain, accurate classification of jobs and resumes to occupation categories is important for matching job seekers with relevant jobs. An example of such a job title classification system is an automatic text document classification system that utilizes machine learning. Machine learning-based document classification techniques(More)
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(More)
CareerBuilder (CB) currently has 50 million active resumes and 2 million active job postings. Our team has been working to provide the most relevant jobs for job seekers and resumes for employers and recruiters. These goals often lead to Big Data problems. In this paper, we introduce WebScalding, a Big Data framework designed and developed to solve some of(More)
Entity linking links entity mentions in text to the corresponding entities in a knowledge base (KB) and has many applications in both open domain and specific domains. For example, in the recruitment domain, linking employer names in job postings or resumes to entities in an employer KB is very important to many business applications. In this paper, we(More)
Eliminating or reducing skill gaps in the job market is critical to putting people back to work, reducing the unemployment rate, and increasing the labor market participation rate. A key element in closing the skills gap is accurately identifying the mismatch between the skills expected by employers and those possessed by job seekers. In this study, our(More)
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