使用BERT进行端到端简历解析并查找职位描述的候选人
End-to-End Resume Parsing and Finding Candidates for a Job Description using BERT
摘要：
申请职位的人数不断增加，这对雇主人工寻找合适的候选人提出了挑战。我们提出了一个端到端的解决方案，根据候选人对职位描述的适合性对候选人进行排名。我们分两个阶段来完成。首先，我们建立一个简历解析器，从候选简历中提取完整的信息。该解析器以Web应用程序的形式向公众提供。其次，我们使用BERT句子对分类来执行排名的基础上，他们的适合性的职位描述。为了近似职位描述，我们使用候选人在简历中提到的过去工作经验的描述。我们的数据集包括LinkedIn格式和一般非LinkedIn格式的简历。我们以100%的准确率解析LinkedIn简历，并为候选人的适合性建立了73%的准确率。
英文摘要：
The ever-increasing number of applications to job positions presents a challenge for employers to find suitable candidates manually. We present an end-to-end solution for ranking candidates based on their suitability to a job description. We accomplish this in two stages. First, we build a resume parser which extracts complete information from candidate resumes. This parser is made available to the public in the form of a web application. Second, we use BERT sentence pair classification to perform ranking based on their suitability to the job description. To approximate the job description, we use the description of past job experiences by a candidate as mentioned in his resume. Our dataset comprises resumes in LinkedIn format and general non-LinkedIn formats. We parse the LinkedIn resumes with 100\% accuracy and establish a strong baseline of 73\% accuracy for candidate suitability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
