research-article

Resume Parsing based on Multi-label Classification using Neural Network models

Authors:

Sujatha krishnamoorthyAuthors Info & Claims

ICBDC '21: Proceedings of the 6th International Conference on Big Data and Computing

Pages 177 - 185

https://doi.org/10.1145/3469968.3469998

Published: 06 October 2021 Publication History

Abstract

Application for jobs usually brings much work for both appliers and HR. Appliers want to apply for the jobs which they are most suitable. The number of applications for a particular position can be significant, making the candidates’ selection cumbersome for HR. Nowadays, hiring processes are often conducted through the Virtual mode with emails. This creates chances for analyzing the data in the resume. Therefore, to enhance selection problems’ efficiency, resume parsing algorithms have been developed in recent years to predict resume-based skills or good jobs quickly. The artificial neural network is a hot spot in the field of artificial intelligence since the 1980s. It abstracts the human brain's neural network from the angle of information processing, establishes some simple models, and forms different networks according to different connection modes. In recent years, neural networks-based algorithms perform high efficiency in processing text classification. This paper put forward some of the efficient algorithms used in text classification, Like BPNN, CNN, BiLSTM, and CRNN, for resume parsing. The original resumes are parsed by splitting them into words, and word base is trained to get the most appropriate word, which has a high score in the resume is resulting suitable job for each resume. The CRNN performs best in resume parsing, which the accuracy can reach 96%. CNN places the lowest accuracy. The BPNN achieves good accuracy but brings inflexible.

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https://doi.org/10.1371/journal.pone.0315768
Jalili ATabrizchi HRazmara JMosavi A(2024)BiLSTM for Resume Classification2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)10.1109/SAMI60510.2024.10432836(000519-000524)Online publication date: 25-Jan-2024
https://doi.org/10.1109/SAMI60510.2024.10432836
Ramyar RNagarani GNatarajan S(2024)Deep Learning based Approach to Streamline Resume Categorization and Ranking2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)10.1109/ICICNIS64247.2024.10823187(840-845)Online publication date: 17-Dec-2024
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Resume Parsing based on Multi-label Classification using Neural Network models
1. Computing methodologies
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ICBDC '21: Proceedings of the 6th International Conference on Big Data and Computing

May 2021

218 pages

ISBN:9781450389808

DOI:10.1145/3469968

Copyright © 2021 ACM.

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Published: 06 October 2021

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ICBDC 2021

ICBDC 2021: 2021 6th International Conference on Big Data and Computing

May 22 - 24, 2021

Shenzhen, China

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Cited By

Lange MKoliousis AFayez FGogarty ETwumasi R(2025)Schizophrenia more employable than depression? Language-based artificial intelligence model ratings for employability of psychiatric diagnoses and somatic and healthy controlsPLOS ONE10.1371/journal.pone.031576820:1(e0315768)Online publication date: 8-Jan-2025
https://doi.org/10.1371/journal.pone.0315768
Jalili ATabrizchi HRazmara JMosavi A(2024)BiLSTM for Resume Classification2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI)10.1109/SAMI60510.2024.10432836(000519-000524)Online publication date: 25-Jan-2024
https://doi.org/10.1109/SAMI60510.2024.10432836
Ramyar RNagarani GNatarajan S(2024)Deep Learning based Approach to Streamline Resume Categorization and Ranking2024 International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS)10.1109/ICICNIS64247.2024.10823187(840-845)Online publication date: 17-Dec-2024
https://doi.org/10.1109/ICICNIS64247.2024.10823187
Thangaramya KLogeswari GGajendran SDeepika Roselind JAhirwar N(2024)Automated Resume Parsing and Ranking using Natural Language Processing2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)10.1109/AIIoT58432.2024.10574696(1-6)Online publication date: 3-May-2024
https://doi.org/10.1109/AIIoT58432.2024.10574696
Kinger SKinger DThakkar SBhake D(2024)Towards smarter hiring: resume parsing and ranking with YOLOv5 and DistilBERTMultimedia Tools and Applications10.1007/s11042-024-18778-983:35(82069-82087)Online publication date: 12-Mar-2024
https://doi.org/10.1007/s11042-024-18778-9
Espinal AHaralambous YBedart DPuentes J(2023)A Format-sensitive BERT-based Approach to Resume Segmentation2023 33rd Conference of Open Innovations Association (FRUCT)10.23919/FRUCT58615.2023.10143072(30-37)Online publication date: 24-May-2023
https://doi.org/10.23919/FRUCT58615.2023.10143072
Pawade DJoshi TParkhe S(2023)Survey on Resume and Job Profile Matching System2023 6th International Conference on Advances in Science and Technology (ICAST)10.1109/ICAST59062.2023.10454929(229-233)Online publication date: 8-Dec-2023
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Srivastava MGreaney P(2023)Resume Parsing Across Multiple Job Domains Using a BERT-Based NER Model2023 31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)10.1109/AICS60730.2023.10470917(1-4)Online publication date: 7-Dec-2023
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