Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.

翻译：衡量职称之间的语义相似性是自动提出职称建议的一个基本功能。 这项任务通常使用监督的学习技术来完成,这要求以同等职称对等形式提供培训数据。 在本文件中,我们建议采用一种不受监督的代议制学习方法,用吵闹的技能标签来培训职称相似模式。 我们表明,对于文本排名和工作正常化等任务来说,这种方法非常有效。