Abstract
The matching of competences, such as skills, occupations or knowledges, is a key desiderata for candidates to be fit for jobs. Automatic extraction of competences from CVs and Jobs can greatly promote recruiters' productivity in locating relevant candidates for job vacancies. This work presents the first model that jointly extracts and classifies competence from Danish job postings. Different from existing works on skill extraction and skill classification, our model is trained on a large volume of annotated Danish corpora and is capable of extracting a wide range of Danish competences, including skills, occupations and knowledges of different categories. More importantly, as a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference. On a real-scenario job matching dataset, our model beats the state-of-the-art models in the overall performance of Danish competence extraction and classification, and saves over 50% time at inference.
Abstract (translated)
胜任力的匹配，如技能、职业或知识，是候选人适合工作的关键要求。从简历和职位描述中自动提取胜任力可以大大提高招聘人员在寻找合适候选人方面的效率。本研究首次提出了一种能够联合提取并分类丹麦语职位发布中的胜任力的模型。与现有的技能抽取及技能分类工作不同的是，我们的模型是在大量注释过的丹麦语文本上进行训练的，并且有能力抽取广泛范围内的丹麦语胜任力，包括各类别的技能、职业和知识。更重要的是，作为一个用于联合提取和分类的类似BERT的单一架构，我们的模型在推理时轻便高效。在一个实际场景下的职位匹配数据集上，我们在整体性能方面超越了现有的最先进的模型，在丹麦语胜任力抽取与分类任务中表现更优，并且推理时间节省超过50%。
URL
https://arxiv.org/abs/2410.22103
