Matt McNair

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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 Recognition (NER) and Named Entity Nor-malization (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 nor-malization of(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)
Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine(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)
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