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 goal was to profile job titles by effectively quantifying the relevance of skills. We started by using a naive, frequency-based skill ranking approach, which resulted in the most generic skills ranked on the top. We then adapted a number of alternative metrics and compared their performances on a number of job titles. The outcome of this study can support CareerCoach, an analytical solution CareerBuilder has piloted to provide insights and data dashboards to job seekers.

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 focus on this employer name normalization task, which has several unique challenges: handling employer names from both job postings and resumes, leveraging the corresponding location context, and handling name variations, irrelevant input data, and noises in the KB. We present a system called CompanyDepot which contains a machine learning based approach CompanyDepot-ML and a heuristic approach CompanyDepot-H to address these challenges in three steps: (1) searching for candidate entities based on a customized search engine for the KB; (2) ranking the candidate entities using learning-to-rank methods or heuristics; and (3) validating the top-ranked entity via binary classification or heuristics. While CompanyDepot-ML shows better extendability and flexibility, CompanyDepot-H serves as a strong baseline and useful way to collect training data for CompanyDepot-ML. The proposed system achieves 2.5%-21.4% higher coverage at the same precision level compared to an existing system used at CareerBuilder over multiple real-world datasets. Applying the system to a similar task of academic institution name normalization further shows the generalization ability of the method.

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 skills is the key for the predictive analysis of labor market dynamics. Such analytics help bridge the skills gap between employers and candidate workers by matching the right talent for the right job and identifying in-demand skills for workforce training programs. This can also work towards the social goal of providing more job opportunities to the community. In this paper we propose an automated approach for skill entity recognition and optimal normalization. The proposed system has two components: 1) Skills taxonomy generation, which employs vocational skill related sections of resumes and Wikipedia categories to define and develop a taxonomy of professional skills; 2) Skills tagging, which leverages properties of semantic word vectors to recognize and normalize relevant skills in input text. By sampling based end-user evaluation, the current system attains 91% accuracy on the taxonomy generation and 82% accuracy on the skills tagging tasks. The beta version of the system is currently applied in various big data and business intelligence applications for workforce analytics and career track projections at CareerBuilder.