Construction of English Resume Corpus and Test with Pre-trained Language Models

TL;DR

This paper constructs an English resume corpus with improved classification rules and larger sample size, and evaluates the performance of pre-trained language models on this dataset, exploring the impact of training sample size on accuracy.

Contribution

It introduces a larger, more fine-grained resume dataset and assesses pre-trained language models' performance, highlighting the effect of training sample size on accuracy.

Findings

Enhanced annotation rules improve dataset accuracy.

Larger training sets lead to higher model correctness.

Pre-trained models perform better with the improved dataset.

Abstract

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.