Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language

Autores
Fernández, Juan Manuel; Cavasi, Nicolás; Errecalde, Marcelo Luis
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Currently, millions of data are generated daily and its exploitation and interpretation has become essential at every scope. However, most of this information is in textual format, lacking the structure and organisation of traditional databases, which represents an enormous challenge to overcome. Over the course of time, different approaches have been proposed for text representation attempting to better capture the semantic of documents. They included classic information retrieval approaches (like Bag of Words) to new approaches based on neural networks such as basic word embeddings, deep learning architectures (LSTMs and CNNs), and contextualized embeddings based on attention mechanisms (Transformers). Unfortunately, most of the available resources supporting those technologies are English-centered. In this work, using an e-mail-based study case, we measure the performance of the three most important machine learning approaches applied to the text classification, in order to verify if new arrivals enhance the results from the Spanish language classification models.
Facultad de Informática
Materia
Ciencias Informáticas
Text Classification
SVM
Word2Vec
LSTM
BERT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125140

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network_name_str SEDICI (UNLP)
spelling Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish languageFernández, Juan ManuelCavasi, NicolásErrecalde, Marcelo LuisCiencias InformáticasText ClassificationSVMWord2VecLSTMBERTCurrently, millions of data are generated daily and its exploitation and interpretation has become essential at every scope. However, most of this information is in textual format, lacking the structure and organisation of traditional databases, which represents an enormous challenge to overcome. Over the course of time, different approaches have been proposed for text representation attempting to better capture the semantic of documents. They included classic information retrieval approaches (like Bag of Words) to new approaches based on neural networks such as basic word embeddings, deep learning architectures (LSTMs and CNNs), and contextualized embeddings based on attention mechanisms (Transformers). Unfortunately, most of the available resources supporting those technologies are English-centered. In this work, using an e-mail-based study case, we measure the performance of the three most important machine learning approaches applied to the text classification, in order to verify if new arrivals enhance the results from the Spanish language classification models.Facultad de Informática2021info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf20-24http://sedici.unlp.edu.ar/handle/10915/125140enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4info:eu-repo/semantics/reference/hdl/10915/121564info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:30:08Zoai:sedici.unlp.edu.ar:10915/125140Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:30:08.434SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
title Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
spellingShingle Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
Fernández, Juan Manuel
Ciencias Informáticas
Text Classification
SVM
Word2Vec
LSTM
BERT
title_short Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
title_full Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
title_fullStr Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
title_full_unstemmed Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
title_sort Classic and recent (neural) approaches to automatic text classification : A comparative study with e-mails in the Spanish language
dc.creator.none.fl_str_mv Fernández, Juan Manuel
Cavasi, Nicolás
Errecalde, Marcelo Luis
author Fernández, Juan Manuel
author_facet Fernández, Juan Manuel
Cavasi, Nicolás
Errecalde, Marcelo Luis
author_role author
author2 Cavasi, Nicolás
Errecalde, Marcelo Luis
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Text Classification
SVM
Word2Vec
LSTM
BERT
topic Ciencias Informáticas
Text Classification
SVM
Word2Vec
LSTM
BERT
dc.description.none.fl_txt_mv Currently, millions of data are generated daily and its exploitation and interpretation has become essential at every scope. However, most of this information is in textual format, lacking the structure and organisation of traditional databases, which represents an enormous challenge to overcome. Over the course of time, different approaches have been proposed for text representation attempting to better capture the semantic of documents. They included classic information retrieval approaches (like Bag of Words) to new approaches based on neural networks such as basic word embeddings, deep learning architectures (LSTMs and CNNs), and contextualized embeddings based on attention mechanisms (Transformers). Unfortunately, most of the available resources supporting those technologies are English-centered. In this work, using an e-mail-based study case, we measure the performance of the three most important machine learning approaches applied to the text classification, in order to verify if new arrivals enhance the results from the Spanish language classification models.
Facultad de Informática
description Currently, millions of data are generated daily and its exploitation and interpretation has become essential at every scope. However, most of this information is in textual format, lacking the structure and organisation of traditional databases, which represents an enormous challenge to overcome. Over the course of time, different approaches have been proposed for text representation attempting to better capture the semantic of documents. They included classic information retrieval approaches (like Bag of Words) to new approaches based on neural networks such as basic word embeddings, deep learning architectures (LSTMs and CNNs), and contextualized embeddings based on attention mechanisms (Transformers). Unfortunately, most of the available resources supporting those technologies are English-centered. In this work, using an e-mail-based study case, we measure the performance of the three most important machine learning approaches applied to the text classification, in order to verify if new arrivals enhance the results from the Spanish language classification models.
publishDate 2021
dc.date.none.fl_str_mv 2021
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info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/reference/hdl/10915/121564
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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