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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125140
Ver los metadatos del registro completo
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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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eng |
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