论文标题
使用弱监督从职位发布中提取技巧
Skill Extraction from Job Postings using Weak Supervision
论文作者
论文摘要
从职位发布获得的汇总数据为劳动力市场需求，新兴技能以及援助工作匹配提供了有力的见解。但是，大多数提取方法都是监督的，因此需要昂贵且耗时的注释。为了克服这一点，我们建议通过弱监督进行技能提取。我们利用欧洲技能，能力，资格和职业分类法通过潜在代表来找到工作广告的类似技能。该方法基于令牌级别和句法模式显示出强的正信号，优于基准。
Aggregated data obtained from job postings provide powerful insights into labor market demands, and emerging skills, and aid job matching. However, most extraction approaches are supervised and thus need costly and time-consuming annotation. To overcome this, we propose Skill Extraction with Weak Supervision. We leverage the European Skills, Competences, Qualifications and Occupations taxonomy to find similar skills in job ads via latent representations. The method shows a strong positive signal, outperforming baselines based on token-level and syntactic patterns.
