# Determining Standard Occupational Classification Codes from Job Descriptions in Immigration Petitions

arXiv: https://arxiv.org/abs/2110.00078 · HTML: https://mlsanity.com/paper/2110.00078/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 30 Sep 2021.

## Builds on (most cited first, 4 of 4)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Empirical Study of Deep Learning for Text Classification in Legal Document Review](https://mlsanity.com/paper/1904.01723/) (2019)
- [Immigration Document Classification and Automated Response Generation](https://mlsanity.com/paper/2010.01997/) (2020)

## Built on by (most cited first, 2 of 2)

- [LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models](https://mlsanity.com/paper/2309.09708/) (2023)
- [Leveraging Large Language Models for Career Mobility Analysis: A Study of Gender, Race, and Job Change Using U.S. Online Resume Profiles](https://mlsanity.com/paper/2511.12010/) (2025)

## Abstract (arXiv)

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.