论文标题
从嘈杂的技能标签中学习工作标题的相似性
Learning Job Titles Similarity from Noisy Skill Labels
论文作者
论文摘要
衡量工作头衔之间的语义相似性是自动工作建议的重要功能。通常使用有监督的学习技术来处理此任务，这需要以同等职位对的形式培训数据。在本文中，我们提出了一种使用嘈杂技能标签培训职务相似性模型的无监督表示学习方法。我们表明，这对于诸如文本排名和工作归一化之类的任务非常有效。
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.
