loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Maísa Kely de Melo 1 ; 2 ; Allan Victor Almeida Faria 1 ; 3 ; Li Weigang 1 ; 4 ; Arthur Gomes Nery 1 ; 5 ; Flávio Augusto R. de Oliveira 6 ; 1 ; Ian Teixeira Barreiro 1 ; 7 and Victor Rafael Rezende Celestino 8 ; 1

Affiliations: 1 LAMFO - Lab. of ML in Finance and Organizations, University of Brasilia, Campus Darcy Ribeiro, Brasilia, Brazil ; 2 Department of Mathematics, Instituto Federal de Minas Gerais Campus Formiga, Formiga, Brazil ; 3 Department of Statistics, University of Brasília, Federal District, Brazil ; 4 Department of Computer Science, University of Brasilia, Campus Darcy Ribeiro, Brasilia, Brazil ; 5 Department of Economics, University of Brasilia, Federal District, Brazil ; 6 Ministry of Science, Technology and Innovation of Brazil, Federal District, Brazil ; 7 Department of Economics, University of São Paulo, Ribeirão Preto, Brazil ; 8 Department of Business Administration, University of Brasilia, Federal District, Brazil

Keyword(s): Automation of Systematic Literature Review, Few-shot Learning, Meta-Learning, Transformers.

Abstract: Systematic Literature Review (SLR) studies aim to leverage relevant insights from scientific publications to achieve a comprehensive overview of the academic progress of a specific field. In recent years, a major effort has been expended in automating the SLR process by extracting, processing, and presenting the synthesized findings. However, implementations capable of few-shot classification for fields of study with a smaller amount of material available seem to be lacking. This study aims to present a system capable of conducting automated systematic literature reviews on classification constraint by a few-shot learning. We propose an open-source, domain-agnostic meta-learning SLR framework for few-shot classification, which has been validated using 64 SLR datasets. We also define an Adjusted Work Saved over Sampling (AWSS) metric to take into account the class imbalance during validation. The initial results show that AWSS@95% scored as high as 0.9 when validating our learner with data from 32 domains (just 16 examples were used for training in each domain), and only four of them resulted in scores lower than 0.1. These findings indicate significant savings in screening time for literature reviewers. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.235.25.27

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Kely de Melo, M.; Faria, A.; Weigang, L.; Nery, A.; R. de Oliveira, F.; Barreiro, I. and Celestino, V. (2022). Few-shot Approach for Systematic Literature Review Classifications. In Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST, ISBN 978-989-758-613-2; ISSN 2184-3252, pages 33-44. DOI: 10.5220/0011526400003318

@conference{webist22,
author={Maísa {Kely de Melo}. and Allan Victor Almeida Faria. and Li Weigang. and Arthur Gomes Nery. and Flávio Augusto {R. de Oliveira}. and Ian Teixeira Barreiro. and Victor Rafael Rezende Celestino.},
title={Few-shot Approach for Systematic Literature Review Classifications},
booktitle={Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST,},
year={2022},
pages={33-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011526400003318},
isbn={978-989-758-613-2},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Web Information Systems and Technologies - WEBIST,
TI - Few-shot Approach for Systematic Literature Review Classifications
SN - 978-989-758-613-2
IS - 2184-3252
AU - Kely de Melo, M.
AU - Faria, A.
AU - Weigang, L.
AU - Nery, A.
AU - R. de Oliveira, F.
AU - Barreiro, I.
AU - Celestino, V.
PY - 2022
SP - 33
EP - 44
DO - 10.5220/0011526400003318