Distributional Semantics Over Semi-structured Data

许多应用程序涉及结构化和非结构化数据。例如，在将作业与 求职者，结构化的组成部分包括所需的正式资格，工作地点， 职称等，而非结构化组件是作业本身的原始文本描述， 候选人的简历。在这个项目中，我们将研究如何将这两个来源的 信息，以便构建能够支持关于该领域的有用推断的语义表示 例如对面试某个职位的候选人进行排名。使用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， 更好的实体概况，如职位空缺或求职者。这些意义表征将被 应用于对求职者进行排名，并提供求职者的摘要，突出他们如何 资格符合职位描述。本文的研究将对Workhoppers的匹配算法有一定的借鉴意义， 使更多的职位候选人能够匹配到合适的职位。它也可以使加拿大经济受益， 加快求职过程。

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.