Distributional Semantics Over Semi-structured Data

许多应用程序既涉及结构化数据，也涉及非结构化数据。例如，在将作业与 求职者，结构化部分包括所需的正式资格、工作地点、 职称等，而非结构化组件是工作本身的原始文本描述和文本描述 应聘者简历的一部分。在这个项目中，我们将研究如何整合这两个源代码 信息，以便构建能够支持关于领域的有用推论的语义表示 比如对应聘者进行排名，面试某一职位。使用来自WorkHoppers的数据集，该公司 将求职者与合同职位匹配，我们将开发统计意义模型，考虑 输入数据中出现的单词以及它们在数据结构中的位置，以便构建 更好的实体概况，如职位空缺或求职者。然后，这些意义表示将是 适用于对职位候选人进行排名，并提供职位候选人的摘要，以突出他们的 任职资格符合工作要求。该研究将通过以下几个方面为Workhop的匹配算法提供帮助 使更多的求职者能够与适当的工作相匹配。它还可以通过以下方式使加拿大经济受益 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.