论文标题
朝着工作转变标签图表以获得更好的职务表示学习
Towards Job-Transition-Tag Graph for a Better Job Title Representation Learning
论文作者
论文摘要
学习工作标题表示的工作主要基于\ textit {Job-Transition Graph}，它是根据人才的工作历史构建的。但是，由于这些记录通常是凌乱的，因此该图非常稀疏，这会影响学到的表示的质量并阻碍进一步的分析。为了解决这个特定问题，我们建议用其他节点来丰富图表，以提高职位表示形式的质量。具体来说，我们构建\ textIt {Job-Transition-tag图}，这是一个包含两种类型的节点的异质图，即作业标题和标签（即与工作职责或功能相关的单词）。沿着这一行，我们将工作标题表示的学习作为学习节点嵌入在\ textit {Job-Transition-tag图}上的任务。两个数据集的实验表明了我们方法的兴趣。
Works on learning job title representation are mainly based on \textit{Job-Transition Graph}, built from the working history of talents. However, since these records are usually messy, this graph is very sparse, which affects the quality of the learned representation and hinders further analysis. To address this specific issue, we propose to enrich the graph with additional nodes that improve the quality of job title representation. Specifically, we construct \textit{Job-Transition-Tag Graph}, a heterogeneous graph containing two types of nodes, i.e., job titles and tags (i.e., words related to job responsibilities or functionalities). Along this line, we reformulate job title representation learning as the task of learning node embedding on the \textit{Job-Transition-Tag Graph}. Experiments on two datasets show the interest of our approach.
