Distributional Semantics Over Semi-structured Data

许多应用程序都涉及结构化和非结构化数据。例如，在将职位与职位相匹配的系统中 对于求职者来说，结构化部分包括所需的正式资格、工作地点、 职位名称等，而非结构化部分是职位本身的原始文本描述和文本 候选人简历的一部分。在这个项目中，我们将研究如何整合这两种来源 信息以构建可以支持有关领域的有用推论的语义表示 例如对某个职位面试的候选人进行排名。使用来自 Workhoppers 的数据集，该公司 将求职者与合同职位相匹配，我们将开发意义统计模型，考虑 输入数据中存在的单词以及它们在数据结构中的位置，以便构建 更好地了解某个实体（例如职位空缺或求职者）的概况。这些含义表示将是 应用于对求职者进行排名，并提供求职者的摘要，突出显示他们的表现 资格符合职位描述。这项研究将有利于 Workhoppers 的匹配算法 使更多的求职者能够匹配到合适的职位。它还可以使加拿大经济受益 加快求职进程。

Many applications involve both structured and unstructured data. For example, in a system that matches jobs to job seekers, the structured component includes the formal qualifications required, the location of the job, the job title, etc., whereas the unstructured component is the raw text description of the job itself and the textual parts of the candidate's resume. In this project, we will investigate how to incorporate both sources of information in order to build a semantic representation that can support useful inferences about the domain such as ranking candidates to interview for a position. Using a data set from Workhoppers, a company that matches job seekers to contract positions, we will develop statistical models of meaning that consider the words that are present in the input data as well as their position within the structure of the data in order to build a better profile of an entity such as a job opening or a job seeker. These meaning representations will then be applied to ranking job candidates, and to present a summary of a job candidate that highlights how their qualifications meet the job description. This research will benefit Workhoppers' matching algorithm by enabling more job candidates to be matched to an appropriate job. It could also benefit Canadian economy by accelerating the job search process.