CSDE Computational Demography Working Group (CDWG) Hosts Jiahui Xu on New Natural Language Processing Models for Automated Coding (5/15/2024)

Posted: 5/10/2024 ()

On 5/15 from 9:00 AM – 10:00 AM, CDWG will host Jiahui Xu to present her research. Jiahui Xu is a Ph.D. candidate in Sociology and Demography at Pennsylvania State University. Her research interests lie in social inequality, quantitative methodology, and computational sociology. Her actively ongoing projects include: 1). adapting the generalized random forests for causal decomposition to investigate college returns; 2). combining machine learning and causal inference methods to decompose health disparities; 3). applying natural language processing models to autocode occupational text data. The event will occur in 223 Raitt (the Demography Lab) and on Zoom (register here). Learn more about the talk in the full story.

Title: From Job Descriptions to Occupations: New Natural Language Processing Models for Automated Coding

Abstract: Occupation is a fundamental concept in social and policy research, but classifying job descriptions into occupational categories can be challenging and susceptible to errors. Traditionally, this involved expert manual coding, translating detailed, often ambiguous job descriptions to standardized categories, a process both laborious and costly. However, recent advances in computational techniques offer efficient automated coding alternatives. Existing autocoding tools, including the O*NET-SOC AutoCoder, the NIOCCS AutoCoder, and the SOCcer AutoCoder, rely on supervised machine learning methods and string-matching algorithms. Yet these autocoders are not designed to understand semantic meanings in occupational write-in text. We develop a new autocoder based on Google’s Text-to-Text Transfer Transformer (T5) model. Like GPT and other large language models, T5 is pretrained on vast amounts of text data. We develop a T5-based occupational classifier (T5-OCC) model with fine-tuned model parameters and training data from occupation write-ins from the 2019 American Community Survey. By comparing our T5-OCC with existing methods, we show that the autocoding accuracy rate increases from 61.8% to 71.1%. Considering the rapid change in neural language models, we conclude by offering suggestions on how to adapt our method for the development of occupational autocoding models in future research.