首个联合抽取丹麦能力的模型，提升招聘匹配效率
Joint Extraction and Classification of Danish Competences for Job Matching
- 基于单个BERT架构联合抽取与分类丹麦能力
- 在真实数据集上性能超越现有模型，推理提速超50%
- 适合需要处理丹麦语简历与职位匹配的招聘系统
能力匹配（如技能、职业、知识）是候选人适配岗位的关键。从简历和职位描述中自动提取能力可显著提升招聘效率。本文提出首个联合抽取与分类丹麦语能力的模型。不同于现有技能提取与分类方法，该模型在大规模标注的丹麦语语料上训练，可识别多种类别的能力，包括技能、职业和知识。更重要的是，作为单一BERT类架构，该模型轻量高效，推理速度快。在真实场景的职位匹配数据集上，其整体表现优于当前最优模型，且推理时间节省超过50%。
原文摘要 · Abstract (English)
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.
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