  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.
