Standard Occupational Classifiers (SOC) are systems used to categorize and classify different types of jobs and occupations based on their similarities in terms of job duties, skills, and qualifications. Integrating these facets with Big Data from job advertisement offers the prospect to investigate labour demand that is specific to various occupations. This project investigates the use of recent developments in natural language processing to construct a classifier capable of assigning an occupation code to a given job advertisement. We develop various classifiers for both UK ONS SOC and US O*NET SOC, using different Language Models. We find that an ensemble model, which combines Google BERT and a Neural Network classifier while considering job title, description, and skills, achieved the highest prediction accuracy. Specifically, the ensemble model exhibited a classification accuracy of up to 61% for the lower (or fourth) tier of SOC, and 72% for the third tier of SOC. This model could provide up to date, accurate information on the evolution of the labour market using job advertisements.

翻译：标准职业分类器（SOC）是一种基于工作职责、技能和资质的相似性，对不同类型职业进行分类和归类的系统。将这些方面与来自招聘广告的大数据相结合，为研究针对各类职业的劳动力需求提供了可能。本项目探讨了利用自然语言处理领域的最新进展，构建一个能够为给定招聘广告分配职业代码的分类器。我们针对英国ONS SOC和美国O*NET SOC，使用不同的语言模型开发了多种分类器。研究发现，结合Google BERT与神经网络分类器，并综合考虑职位名称、描述和技能的集成模型，实现了最高的预测准确率。具体而言，该集成模型在SOC第四级（较低层级）的分类准确率最高可达61%，在第三级可达72%。该模型能够利用招聘广告提供关于劳动力市场演变的实时、准确信息。