用NLP自动分类招聘广告中的职业，提升劳动力市场分析精度
Standard Occupation Classifier -- A Natural Language Processing Approach
- 融合标题、描述和技能，用BERT与神经网络构建集成模型
- 对英国和美国职业分类系统，第三级准确率达72%，第四级达61%
- 适合关注劳动力趋势、职业匹配与招聘数据分析的研究者
标准职业分类（SOC）是根据工作职责、技能和资格相似性对职业进行分类的系统。结合招聘广告的海量数据，可深入分析特定职业的劳动力需求。本研究利用自然语言处理最新进展，构建能为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开发了多种基于不同语言模型的分类器。结果显示，融合Google BERT与神经网络的集成模型，在考虑职位标题、描述和技能的前提下表现最佳，对SOC第四级分类准确率达到61%，第三级达72%。该模型可基于招聘广告实时提供精准的劳动力市场演化信息。
原文摘要 · Abstract (English)
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.
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