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dc.contributor.authorGutiérrez Choque, Anyelo Carlos
dc.contributor.authorMedina Mamani, Vivian
dc.contributor.authorCastro Gutiérrez, Eveling
dc.contributor.authorNúñez Pacheco, Rosa
dc.contributor.authorAguaded, José Ignacio 
dc.date.accessioned2023-04-28T10:18:49Z
dc.date.available2023-04-28T10:18:49Z
dc.date.issued2022
dc.identifier.citationGutiérrez-Choque, A.-C., Medina-Mamani, V., Castro-Gutiérrez, E., Nuñez-Pacheco, R., & Aguaded, I. (2022). Transformer based Model for Coherence Evaluation of Scientific Abstracts: Second Fine-tuned BERT. In International Journal of Advanced Computer Science and Applications (Vol. 13, Issue 5). The Science and Information Organization. https://doi.org/10.14569/ijacsa.2022.01305105es_ES
dc.identifier.issn2158-107X
dc.identifier.issn2156-5570 (electrónico)
dc.identifier.urihttps://hdl.handle.net/10272/22012
dc.description.abstractCoherence evaluation is a problem related to the area of natural language processing whose complexity lies mainly in the analysis of the semantics and context of the words in the text. Fortunately, the Bidirectional Encoder Representation from Transformers (BERT) architecture can capture the aforementioned variables and represent them as embeddings to perform Fine-tunings. The present study proposes a Second Fine-Tuned model based on BERT to detect inconsistent sentences (coherence evaluation) in scientific abstracts written in English/Spanish. For this purpose, 2 formal methods for the generation of inconsistent abstracts have been proposed: Random Manipulation (RM) and K-means Random Manipulation (KRM). Six experiments were performed; showing that performing Second Fine-Tuned improves the detection of inconsistent sentences with an accuracy of 71%. This happens even if the new retraining data are of different language or different domain. It was also shown that using several methods for generating inconsistent abstracts and mixing them when performing Second Fine-Tuned does not provide better results than using a single technique.es_ES
dc.description.sponsorshipTo the Universidad Nacional de San Agust´ın de Arequipa for the funding granted to the project ”Transmedia, Gamification and Video games to promote scientific writing in Engineering students”, under Contract No. IBA-IB-38-2020-UNSA. We would like to thank to the ”Research Center, Transfer of Technologies and Software Development R+D+i” -CiTeSoft-EC-0003-2017-UNSA, for their collaboration in the use of their equipment and facilities, for the development of this research work.
dc.language.isoenges_ES
dc.publisherThe Science and Information Organizationes_ES
dc.relation.isversionofPublisher’s version
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherCoherence evaluationes_ES
dc.subject.otherInconsistent sentences detectiones_ES
dc.subject.otherBERTes_ES
dc.subject.otherSecond fine-tunedes_ES
dc.titleTransformer based Model for Coherence Evaluation of Scientific Abstracts: Second Fine-tuned BERTes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.14569/IJACSA.2022.01305105
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subject.unesco5701 Lingüística Aplicadaes_ES


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