■准确的职业分类在各个领域至关重要,包括政策制定和流行病学研究。本研究旨在开发基于DistilKoBERT的职业分类模型。
■这项研究使用了分别于2017年和2020年进行的第5次和第6次韩国工作条件调查的数据。共有99,665名调查参与者,他们是韩国工人的全国代表,包括在内。我们根据韩国标准职业分类(第7版,3位数字代码)。数据集以7:3的比例随机分为训练和测试数据集。利用训练数据集对基于DistilKoBERT的职业分类模型进行了微调,并使用测试数据集对模型进行评估。准确性,精度,召回,计算F1评分作为评价指标。
■最终模型,将测试数据集中的28,996名调查参与者分为142个职业代码,准确率为84.44%。对于评估指标,精度,召回,和模型的F1得分,根据样本量加权计算,分别为0.83、0.84和0.83。该模型在服务和销售人员的分类中表现出很高的精度,但在经理的分类中却表现出很低的精度。此外,它在对训练数据集中突出表现的职业进行分类时显示出很高的精度。
■这项研究开发了一种基于DistilKoBERT的职业分类系统,表现合理。尽管进一步努力提高分类精度,这种自动职业分类模型有望促进职业安全和卫生领域的流行病学研究。
UNASSIGNED: Accurate occupation classification is essential in various fields, including policy development and epidemiological studies. This study aims to develop an occupation classification model based on DistilKoBERT.
UNASSIGNED: This study used data from the 5th and 6th Korean Working Conditions Surveys conducted in 2017 and 2020, respectively. A total of 99,665 survey participants, who were nationally representative of Korean workers, were included. We used natural language responses regarding their job responsibilities and occupational codes based on the Korean Standard Classification of Occupations (7th version, 3-digit codes). The dataset was randomly split into training and test datasets in a ratio of 7:3. The occupation classification model based on DistilKoBERT was fine-tuned using the training dataset, and the model was evaluated using the test dataset. The accuracy, precision, recall, and F1 score were calculated as evaluation metrics.
UNASSIGNED: The final model, which classified 28,996 survey participants in the test dataset into 142 occupational codes, exhibited an accuracy of 84.44%. For the evaluation metrics, the precision, recall, and F1 score of the model, calculated by weighting based on the sample size, were 0.83, 0.84, and 0.83, respectively. The model demonstrated high precision in the classification of service and sales workers yet exhibited low precision in the classification of managers. In addition, it displayed high precision in classifying occupations prominently represented in the training dataset.
UNASSIGNED: This study developed an occupation classification system based on DistilKoBERT, which demonstrated reasonable performance. Despite further efforts to enhance the classification accuracy, this automated occupation classification model holds promise for advancing epidemiological studies in the fields of occupational safety and health.