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Natural language contents evaluation system for multi-class news categorization using machine learning and transformers

dc.contributor.authorMarrugo, Duván A
dc.contributor.authorMartinez-Santos, Juan Carlos
dc.contributor.authorPuertas, Edwin
dc.date.accessioned2023-12-05T16:10:06Z
dc.date.available2023-12-05T16:10:06Z
dc.date.issued2023-12-05
dc.date.submitted2023-12-05
dc.identifier.citationMarrugo, 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.spa
dc.identifier.urihttps://hdl.handle.net/20.500.12585/12578
dc.description.abstractThe 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.spa
dc.description.sponsorshipUniversidad Tecnlógica de Bolívarspa
dc.format.extent12 páginas
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.sourceApplied Computer Sciences in Engineeringspa
dc.titleNatural language contents evaluation system for multi-class news categorization using machine learning and transformersspa
dcterms.bibliographicCitationlab 912, M.: Deeplearning hw2 transformer (2022). https://kaggle.com/ competitions/deeplearning-hw2-transformerspa
dcterms.bibliographicCitationAhmed, J., Ahmed, M.: Online news classification using machine learning tech niques. IIUM Eng. J. 22, 210–225 (2021). https://doi.org/10.31436/iiumej.v22i2. 1662, https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1662spa
dcterms.bibliographicCitationAhmed, J., Ahmed, M.: Online news classification using machine learning tech niques. IIUM Eng. J. 22, 210–225 (2021). https://doi.org/10.31436/iiumej.v22i2. 1662, https://journals.iium.edu.my/ejournal/index.php/iiumej/article/view/1662spa
dcterms.bibliographicCitationPatro, A., Mahima Patel, R.S., Save, D.J.: Real time news classification using machine learning. Int. J. Adv. Sci. Technol. 29(9s), 620–630 (2020)spa
dcterms.bibliographicCitationBarua, A., Sharif, O., Hoque, M.M.: Multi-class sports news categorization using machine learning techniques: resource creation and evaluation. Proce dia Compute. Sci. 193, 112–121 (2021). https://doi.org/10.1016/j.procs.2021.11. 002, https://www.sciencedirect.com/science/article/pii/S1877050921021268. 10th International Young Scientists Conference in Computational Science, YSC2021, 28 June–2 July 2021spa
dcterms.bibliographicCitationBlackledge, C., Atapour-Abarghouei, A.: Transforming fake news: robust gener alisable news classification using transformers (2021). https://doi.org/10.48550/ ARXIV.2109.09796, http://arxiv.org/2109.09796spa
dcterms.bibliographicCitationCho, K., et al.: Learning phrase representations using RNN encoder-decoder for sta tistical machine translation (2014). https://doi.org/10.48550/ARXIV.1406.1078, http://arxiv.org/1406.1078spa
dcterms.bibliographicCitationDeb, N., Jha, V., Panjiyar, A., Gupta, R.: A comparative analysis of news catego rization using machine learning approaches. Int. J. Sci. Technol. Res. 9, 2469–2472 (2020)spa
dcterms.bibliographicCitationDevi, J.S., Bai, D.M.R., Reddy, C.: Newspaper article classification using machine learning techniques. Int. J. Innov. Technol. Explor. Eng. 9(5), 872–877 (2020). https://doi.org/10.35940/ijitee.e2753.039520, https://dx.doi.org/10.35940/ijitee.E2753.039520spa
dcterms.bibliographicCitationDevlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding (2018). https://doi.org/10. 48550/ARXIV.1810.04805, http://arxiv.org/1810.04805 11. Elnagar, A., Al-Debsi, R., Einea, O.: Arabic text classification using deep learning models. Inf. Process. Manag. 57(1), 102121 (2020). https://doi.org/ 10.1016/j.ipm.2019.102121, https://www.sciencedirect.com/science/article/pii/ S0306457319303413 12. Gillioz, A., Casas, J., Mugellini, E., Khaled, O.A.: Overview of the transformer based models for NLP tasks. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 179–183 (2020). https://doi.org/10.15439/ 2020F20spa
dcterms.bibliographicCitationElnagar, A., Al-Debsi, R., Einea, O.: Arabic text classification using deep learning models. Inf. Process. Manag. 57(1), 102121 (2020). https://doi.org/ 10.1016/j.ipm.2019.102121, https://www.sciencedirect.com/science/article/pii/ S0306457319303413spa
dcterms.bibliographicCitationillioz, A., Casas, J., Mugellini, E., Khaled, O.A.: Overview of the transformer based models for NLP tasks. In: 2020 15th Conference on Computer Science and Information Systems (FedCSIS), pp. 179–183 (2020). https://doi.org/10.15439/ 2020F20spa
dcterms.bibliographicCitationGreff, K., Srivastava, R.K., Koutnik, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222–2232 (2017). https://doi.org/10.1109/tnnls.2016.2582924spa
dcterms.bibliographicCitationKosheleva, O., Kreinovich, V., Shahbazova, S.: Type-2 fuzzy analysis explains ubiquity of triangular and trapezoid membership functions. In: Shahbazova, S.N., Kacprzyk, J., Balas, V.E., Kreinovich, V. (eds.) Recent Developments and the New Direction in Soft-Computing Foundations and Applications. SFSC, vol. 393, pp. 63–75. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-47124-8 6spa
dcterms.bibliographicCitationLilleberg, J., Zhu, Y., Zhang, Y.: Support vector machines and word2vec for text classification with semantic features. In: 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), pp. 136–140 (2015). https://doi.org/10.1109/ICCI-CC.2015.7259377spa
dcterms.bibliographicCitationLuo, X.: Efficient English text classification using selected machine learning tech niques. Alex. Eng. J. 60(3), 3401–3409 (2021). https://doi.org/10.1016/j.aej.2021. 02.009, https://www.sciencedirect.com/science/article/pii/S1110016821000806spa
dcterms.bibliographicCitationMunikar, M., Shakya, S., Shrestha, A.: Fine-grained sentiment classification using BERT. In: 2019 Artificial Intelligence for Transforming Business and Society (AITB), vol. 1, pp. 1–5 (2019). https://doi.org/10.1109/AITB48515.2019.8947435spa
dcterms.bibliographicCitationPennington, J., Socher, R., Manning, C.: GloVe: global vectors for word repre sentation. In: Proceedings of the 2014 Conference on Empirical Methods in Nat ural Language Processing (EMNLP), pp. 1532–1543. Association for Computa tional Linguistics, Doha (2014). https://doi.org/10.3115/v1/D14-1162, https:// www.aclanthology.org/D14-1162spa
dcterms.bibliographicCitationQadi, L.A., Rifai, H.E., Obaid, S., Elnagar, A.: Arabic text classification of news articles using classical supervised classifiers. In: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS), pp. 1–6 (2019). https://doi.org/ 10.1109/ICTCS.2019.8923073spa
dcterms.bibliographicCitationRustamov, S., Mustafayev, E., Clements, M.: Context analysis of customer requests using a hybrid adaptive neuro fuzzy inference system and hidden Markov models in the natural language call routing problem. Open Eng. 8, 61–68 (2018). https:// doi.org/10.1515/eng-2018-0008spa
dcterms.bibliographicCitationSanh, V., Debut, L., Chaumond, J., Wolf, T.: DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter (2019). https://doi.org/10.48550/ ARXIV.1910.01108, http://arxiv.org/1910.01108spa
dcterms.bibliographicCitationVaswani, A., et al.: Attention is all you need (2017). https://doi.org/10.48550/ ARXIV.1706.03762, http://arxiv.org/1706.03762spa
dcterms.bibliographicCitationYang, Y., Chen, X., Tan, R., Xiao, Y.: IoT Technologies and Applications, pp. 1–60. Wiley (2021). https://doi.org/10.1002/9781119593584.ch1spa
dcterms.bibliographicCitationYıldırım, S., Jothimani, D., Kavaklıoˇglu, C., Ba¸sar, A.: Classification of “hot news” for financial forecast using NLP techniques. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 4719–4722 (2018). https://doi.org/10.1109/BigData. 2018.8621903spa
datacite.rightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bccespa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.identifier.doi10.1007/978-3-031-46739-4_11
dc.subject.keywordsText Classificationspa
dc.subject.keywordsAutomatic Classificationspa
dc.subject.keywordsNews Classificationspa
dc.subject.keywordsTransformerspa
dc.subject.keywordsMachine Learningspa
dc.subject.keywordsDeep Learningspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameUniversidad Tecnológica de Bolívarspa
dc.identifier.reponameRepositorio Universidad Tecnológica de Bolívarspa
dc.publisher.placeCartagena de Indiasspa
dc.subject.armarcLEMB
dc.type.spahttp://purl.org/coar/resource_type/c_6501spa
dc.audiencePúblico generalspa
dc.publisher.sedeCampus Tecnológicospa
oaire.resourcetypehttp://purl.org/coar/resource_type/c_6501spa
dc.publisher.disciplineMaestría en Ingenieríaspa


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Universidad Tecnológica de Bolívar - 2017 Institución de Educación Superior sujeta a inspección y vigilancia por el Ministerio de Educación Nacional. Resolución No 961 del 26 de octubre de 1970 a través de la cual la Gobernación de Bolívar otorga la Personería Jurídica a la Universidad Tecnológica de Bolívar.