论文标题
构建英语简历语料库并使用预训练的语言模型进行测试
Construction of English Resume Corpus and Test with Pre-trained Language Models
论文作者
论文摘要
信息提取（IE）一直是NLP的重要任务之一。此外，信息提取的最关键应用程序方案之一是简历的信息提取。通过对简历的每个部分进行分类来获得构造的文本。存储这些文本以供以后进行搜索和分析很方便。此外，构造的简历数据也可以在AI简历筛选系统中使用。大大降低人力资源的劳动成本。这项研究旨在将简历的信息提取任务转变为简单的句子分类任务。基于先前研究产生的英语简历数据集。改进了分类规则，以创建简历的更大，更细粒度的分类数据集。 This corpus is also used to test some current mainstream Pre-training language models (PLMs) performance.Furthermore, in order to explore the relationship between the number of training samples and the correctness rate of the resume dataset, we also performed comparison experiments with training sets of different train set sizes.The final multiple experimental results show that the resume dataset with improved annotation rules and increased sample size of the dataset improves the accuracy of the original简历数据集。
Information extraction(IE) has always been one of the essential tasks of NLP. Moreover, one of the most critical application scenarios of information extraction is the information extraction of resumes. Constructed text is obtained by classifying each part of the resume. It is convenient to store these texts for later search and analysis. Furthermore, the constructed resume data can also be used in the AI resume screening system. Significantly reduce the labor cost of HR. This study aims to transform the information extraction task of resumes into a simple sentence classification task. Based on the English resume dataset produced by the prior study. The classification rules are improved to create a larger and more fine-grained classification dataset of resumes. This corpus is also used to test some current mainstream Pre-training language models (PLMs) performance.Furthermore, in order to explore the relationship between the number of training samples and the correctness rate of the resume dataset, we also performed comparison experiments with training sets of different train set sizes.The final multiple experimental results show that the resume dataset with improved annotation rules and increased sample size of the dataset improves the accuracy of the original resume dataset.
