Named entity recognition in resumes

TL;DR

This paper presents a deep learning-based semi-automatic system for named entity recognition in resumes, focusing on IT industry resumes, to automate extraction of key information and improve efficiency.

Contribution

It adapts six transformer-based pre-trained models for NER in resumes, achieving high accuracy in recognizing eight entity types specific to IT resumes.

Findings

RoBERTa achieved the highest micro and weighted F1 scores.

Electra achieved the highest macro F1 score.

The system effectively automates information extraction from resumes.

Abstract

Named entity recognition (NER) is used to extract information from various documents and texts such as names and dates. It is important to extract education and work experience information from resumes in order to filter them. Considering the fact that all information in a resume has to be entered to the companys system manually, automatizing this process will save time of the companies. In this study, a deep learning-based semi-automatic named entity recognition system has been implemented with a focus on resumes in the field of IT. Firstly, resumes of employees from five different IT related fields has been annotated. Six transformer based pre-trained models have been adapted to named entity recognition problem using the annotated data. These models have been selected among popular models in the natural language processing field. The obtained system can recognize eight different entity types which are city, date, degree, diploma major, job title, language, country and skill. Models used in the experiments are compared using micro, macro and weighted F1 scores and the performance of the methods was evaluated. Taking these scores into account for test set the best micro and weighted F1 score is obtained by RoBERTa and the best macro F1 score is obtained by Electra model.