SME credit application, a text classification approach

Detalhes bibliográficos
Autor(a) principal: López, Daniela Saavedra
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/103801
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM
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spelling SME credit application, a text classification approachNatural Language Processing (NLP)BankingCredit applicationSmall and medium enterprise (SME)Neural Networks (NN)Bi-directional Encoder Representations for Transformers (BERT)Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMDuring the SME credit application process a credit expert will give a specific recommendation to the credit commercial advisor. This recommendation can be classified as positive, negative or partial. This project aims to construct a text classifier model in order to give the recommendation text one of the categories mentioned before. To achieve this, two models are tested using state-of-the-art architecture called BERT proposed by Google in 2019. The first model will use single sentence BERT classification model as proposed by Google. The second model will use SBERT architecture, where BERT embedding model will be fine-tuned for the specific task, a max-pooling layer is added to extract a fixed size vector for all the document and work under fully connected network architecture. Results show that the second approach got better results regarding accuracy, precision and recall. Despite of the bunch of limitations of computational capacity, limited number of tagged examples and BERT maximum sequence length the model show a good first approach to solve the current problem.Castelli, MauroRUNLópez, Daniela Saavedra2020-09-09T14:13:59Z2020-07-132020-07-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/103801TID:202516008enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:49:28Zoai:run.unl.pt:10362/103801Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:04.650796Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv SME credit application, a text classification approach
title SME credit application, a text classification approach
spellingShingle SME credit application, a text classification approach
López, Daniela Saavedra
Natural Language Processing (NLP)
Banking
Credit application
Small and medium enterprise (SME)
Neural Networks (NN)
Bi-directional Encoder Representations for Transformers (BERT)
title_short SME credit application, a text classification approach
title_full SME credit application, a text classification approach
title_fullStr SME credit application, a text classification approach
title_full_unstemmed SME credit application, a text classification approach
title_sort SME credit application, a text classification approach
author López, Daniela Saavedra
author_facet López, Daniela Saavedra
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv López, Daniela Saavedra
dc.subject.por.fl_str_mv Natural Language Processing (NLP)
Banking
Credit application
Small and medium enterprise (SME)
Neural Networks (NN)
Bi-directional Encoder Representations for Transformers (BERT)
topic Natural Language Processing (NLP)
Banking
Credit application
Small and medium enterprise (SME)
Neural Networks (NN)
Bi-directional Encoder Representations for Transformers (BERT)
description Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM
publishDate 2020
dc.date.none.fl_str_mv 2020-09-09T14:13:59Z
2020-07-13
2020-07-13T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/103801
TID:202516008
url http://hdl.handle.net/10362/103801
identifier_str_mv TID:202516008
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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