| dc.contributor.author | Durán, Claudia | |
| dc.contributor.author | Arias, Deccy | |
| dc.contributor.author | Rosado, Alveiro | |
| dc.coverage.spatial | Colombia | en_US |
| dc.date.accessioned | 2025-04-20T16:07:50Z | |
| dc.date.available | 2025-04-20T16:07:50Z | |
| dc.date.issued | 2024-06-23 | |
| dc.identifier.issn | 1692-7257 | en_US |
| dc.identifier.uri | https://repositorioinstitucional.ufpso.edu.co/xmlui/handle/20.500.14167/5094 | |
| dc.description.abstract | Esta investigación se centró en la automatización de la comprensión e identificación semántica de hallazgos para su clasificación en auditorías internas, utilizando técnicas de procesamiento de lenguaje natural. Se analizaron informes de auditorías internas para extraer textos vinculados a no conformidades, fortalezas y oportunidades de mejora. Para optimizar la presentación del texto para diversos algoritmos, se examinaron métodos como bolsa de palabras (BoW), frecuencia de término-frecuencia inversa de
documento (TF-IDF), así como representaciones de texto a través de vectores de palabras incrustadas como Word2Vec y FastText. Se determinó que la mejor combinación de rendimiento provino de un clasificador lineal, que utiliza datos transformados mediante palabras incrustadas y equilibra las clases con sobre-muestreo. Este modelo fundamenta sus clasificaciones en palabras que capturan adecuadamente el sentido y contexto del hallazgo analizado. | en_US |
| dc.language.iso | spa | en_US |
| dc.relation | file:///C:/Users/maria/Downloads/Optimizaci%C3%B3n%20de%20la%20generaci%C3%B3n%20de%20informes%20de%20auditor%C3%ADa%20mediante%20procesamiento%20de%20lenguaje%20natural_%20un%20enfoque%20basado%20en%20datos%20de%20auditor%C3%ADas%20de%20calidad%20en%20educaci%C3%B3n%20superior.pdf | en_US |
| dc.title | Optimización de la generación de informes de auditoría mediante procesamiento de lenguaje natural: un enfoque basado en datos de auditorías de calidad en educación superior | en_US |
| dc.title.translated | Optimizing audit reporting using natural language processing: a data-driven approach from quality audits in higher education | en_US |
| dc.contributor.cvlac | [0001380164] | en_US |
| dc.contributor.googlescholar | [XszWRgQAAAAJ] | en_US |
| dc.contributor.orcid | [0000-0001-9291-7841] | en_US |
| dc.contributor.researchgate | [Claudia-Chinchilla] | en_US |
| dc.contributor.scopus | [57369586400] | en_US |
| dc.description.abstractenglish | This research focuses on automating the semantic understanding and identification of findings for classification in internal audits, using natural language processing techniques. Internal audit reports were analyzed to extract texts linked to nonconformities, strengths, and opportunities for improvement. To optimize text presentation for various algorithms, methods such as bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and text representations through word embedding vectors such as Word2Vec and FastText were examined. The best performance combination was determined to be provided by a linear classifier, which uses data transformed by word embeddings and balances classes with oversampling. This model bases its classifications on words that adequately capture the meaning and context of the analyzed finding. | en_US |
| dc.subject.proposal | Aprendizaje automático | en_US |
| dc.subject.proposal | auditoría interna | en_US |
| dc.subject.proposal | aprendizaje supervisado | en_US |
| dc.subject.proposal | inteligencia artificial | en_US |
| dc.subject.proposal | procesamiento del lenguaje natural | en_US |
| dc.subject.keywords | Machine learning | en_US |
| dc.subject.keywords | internal audit | en_US |
| dc.subject.keywords | supervised learning | en_US |
| dc.subject.keywords | artificial intelligence | en_US |
| dc.subject.keywords | natural language processing | en_US |
| dc.subject.lemb | Ciencias Sociales | en_US |
| dc.publisher.faculty | Facultad educación artes y humanidades | en_US |
| dc.type.redcol | http://purl.org/redcol/resource_type/ART | en_US |
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