Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions
This addresses a tedious and critical task for immigration officials and applicants, but it is incremental as it applies existing NLP methods to a new domain.
The paper tackled the problem of automatically determining Standard Occupational Classification (SOC) codes from job descriptions in U.S. work visa applications, applying natural language processing methods and identifying the best predictive models based on quality and training time.
Accurate specification of standard occupational classification (SOC) code is critical to the success of many U.S. work visa applications. Determination of correct SOC code relies on careful study of job requirements and comparison to definitions given by the U.S. Bureau of Labor Statistics, which is often a tedious activity. In this paper, we apply methods from natural language processing (NLP) to computationally determine SOC code based on job description. We implement and empirically evaluate a broad variety of predictive models with respect to quality of prediction and training time, and identify models best suited for this task.