Natural language contents evaluation system for multi-class news categorization using machine learning and transformers
The exponential growth of digital documents has come with rapid progress in text classification techniques in recent years. This paper provides text classification models, which analyze various steps of news classification, where some algorithmic approaches for machine learning, such as Logistic Reg...
- Autores:
-
Marrugo, Duván A
Martinez-Santos, Juan Carlos
Puertas, Edwin
- Tipo de recurso:
- Fecha de publicación:
- 2023
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12578
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12578
- Palabra clave:
- Text Classification
Automatic Classification
News Classification
Transformer
Machine Learning
Deep Learning
LEMB
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| title |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| spellingShingle |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers Text Classification Automatic Classification News Classification Transformer Machine Learning Deep Learning LEMB |
| title_short |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| title_full |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| title_fullStr |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| title_full_unstemmed |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| title_sort |
Natural language contents evaluation system for multi-class news categorization using machine learning and transformers |
| dc.creator.fl_str_mv |
Marrugo, Duván A Martinez-Santos, Juan Carlos Puertas, Edwin |
| dc.contributor.author.none.fl_str_mv |
Marrugo, Duván A Martinez-Santos, Juan Carlos Puertas, Edwin |
| dc.subject.keywords.spa.fl_str_mv |
Text Classification Automatic Classification News Classification Transformer Machine Learning Deep Learning |
| topic |
Text Classification Automatic Classification News Classification Transformer Machine Learning Deep Learning LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
The exponential growth of digital documents has come with rapid progress in text classification techniques in recent years. This paper provides text classification models, which analyze various steps of news classification, where some algorithmic approaches for machine learning, such as Logistic Regression, Support Vector Machine, and Random Forest, are implemented. In turn, the uses of Transformers as classification models for the solution of the same problem, proposing BERT and DistilBERT as possible solutions to compare for the automatic classification of news containing articles belonging to four categories (World, Sports, Business, and Science/Technology). We obtained the highest accuracy on the machine learning side, with 88% using Support Vector Machine with Word2Vec. However, using Transformer DistilBERT, we got an efficient model in terms of performance and 91.7% accuracy for classifying news. |
| publishDate |
2023 |
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2023-12-05T16:10:06Z |
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2023-12-05T16:10:06Z |
| dc.date.issued.none.fl_str_mv |
2023-12-05 |
| dc.date.submitted.none.fl_str_mv |
2023-12-05 |
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http://purl.org/coar/version/c_b1a7d7d4d402bcce http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_3248 |
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info:eu-repo/semantics/bookPart |
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Marrugo, D. A., Martinez-Santos, J. C., & Puertas, E. (2023, October). Natural Language Contents Evaluation System for Multi-class News Categorization Using Machine Learning and Transformers. In Workshop on Engineering Applications (pp. 115-126). Cham: Springer Nature Switzerland. |
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https://hdl.handle.net/20.500.12585/12578 |
| dc.identifier.doi.none.fl_str_mv |
10.1007/978-3-031-46739-4_11 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Marrugo, D. A., Martinez-Santos, J. C., & Puertas, E. (2023, October). Natural Language Contents Evaluation System for Multi-class News Categorization Using Machine Learning and Transformers. In Workshop on Engineering Applications (pp. 115-126). Cham: Springer Nature Switzerland. 10.1007/978-3-031-46739-4_11 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12578 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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openAccess |
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12 páginas |
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application/pdf |
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Cartagena de Indias |
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Campus Tecnológico |
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Maestría en Ingeniería |
| dc.source.spa.fl_str_mv |
Applied Computer Sciences in Engineering |
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Universidad Tecnológica de Bolívar |
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Marrugo, Duván A05d2d5f3-e9bd-4c5d-a546-f436ce989b7dMartinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe77318Puertas, Edwin9e3c6f17-9041-40e3-a5fb-929a21d229012023-12-05T16:10:06Z2023-12-05T16:10:06Z2023-12-052023-12-05Marrugo, D. A., Martinez-Santos, J. C., & Puertas, E. (2023, October). Natural Language Contents Evaluation System for Multi-class News Categorization Using Machine Learning and Transformers. In Workshop on Engineering Applications (pp. 115-126). Cham: Springer Nature Switzerland.https://hdl.handle.net/20.500.12585/1257810.1007/978-3-031-46739-4_11Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarThe exponential growth of digital documents has come with rapid progress in text classification techniques in recent years. This paper provides text classification models, which analyze various steps of news classification, where some algorithmic approaches for machine learning, such as Logistic Regression, Support Vector Machine, and Random Forest, are implemented. In turn, the uses of Transformers as classification models for the solution of the same problem, proposing BERT and DistilBERT as possible solutions to compare for the automatic classification of news containing articles belonging to four categories (World, Sports, Business, and Science/Technology). We obtained the highest accuracy on the machine learning side, with 88% using Support Vector Machine with Word2Vec. However, using Transformer DistilBERT, we got an efficient model in terms of performance and 91.7% accuracy for classifying news.Universidad Tecnlógica de Bolívar12 páginasapplication/pdfengApplied Computer Sciences in EngineeringNatural language contents evaluation system for multi-class news categorization using machine learning and transformersinfo:eu-repo/semantics/bookParthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_3248Text ClassificationAutomatic ClassificationNews ClassificationTransformerMachine LearningDeep LearningLEMBinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cartagena de IndiasCampus TecnológicoMaestría en IngenieríaPúblico generallab 912, M.: Deeplearning hw2 transformer (2022). https://kaggle.com/ competitions/deeplearning-hw2-transformerAhmed, J., Ahmed, M.: Online news classification using machine learning tech niques. 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