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.

翻译：信息抽取一直是自然语言处理的核心任务之一，而简历信息抽取是其最重要的应用场景之一。通过将简历各组成部分分类，可获得结构化的文本数据。这些文本便于存储、检索与分析，同时可用于构建人工智能简历筛选系统，显著降低人力资源的人工成本。本研究旨在将简历信息抽取任务转化为简单的句子分类任务。基于前期研究构建的英文简历数据集，通过改进分类规则，创建了一个规模更大、粒度更细的简历分类数据集。该语料库还用于测试当前主流的预训练语言模型的性能。此外，为探究训练样本数量与简历数据集正确率之间的关系，我们开展了不同训练集规模的对比实验。多组实验结果表明，改进标注规则并扩充样本量的简历数据集，其准确率相较于原始简历数据集有所提升。