Browsing by Author "Santos León, David Enrique"

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Construcción de un corpus de gran escala en el idioma español cuyos documentos reflejen opiniones respecto a productos textiles
(Universidad de Cuenca, 2021-11-08) Santos León, David Enrique; Auquilla Sangolquí, Andrés Vinicio
Currently, there is a boom in introducing Machine Learning models to various aspects of everyday life. A relevant field consists of Natural Language Processing (NLP) that seeks to model human language. The difficulty of training models to learn a language is high. A key and basic component for these intelligences to learn properly consists of the data, which in the case of NLP, is mostly in English. This research project arises from the problem of finding large-scale inputs, in languages other than English, to feed Deep Learning models that produce texts automatically. Four main results have been generated: 1) A methodology to build a large-scale corpus, easily scalable to different domains and languages, 2) A corpus in Spanish, within the domain of comments on textile products, with more than 170 thousand documents that obtained good results from human and automatic evaluations, 3) A computational system that automated the construction of the corpus from beginning to end, from the collection of documents to their evaluation, and 4) baseline results of a generational model that serve as a point of reference for future research within the automatic generation of texts within the textile domain
Evaluating functional suitability of domain-specific languages oriented to create interactions for cognitive training
(Institute of Electrical and Electronics Engineers, 2021) Santos León, David Enrique; Maxi Jara, Kevin Alexander; Prado Cabrera, Katerine Daniela; Mora Aguirre, Brian Oswaldo; Valdez Solis, Wilson Fernando; Cedillo Orellana, Irene Priscila
To address problems related to people's memory, several techniques have been developed; one of them consists of cognitive training that seeks to maintain and improve the brain's essential executive functions. Today this technique has made use of software applications to provide such training. One way to support these applications' development is through Domain-Specific Languages (DSLs), which has reduced focus but produces an efficient solution. However, the applications' growth and complexity have generated systematic and specific approaches to evaluating the products developed. This study proposes to assess DSLs aimed at creating interactions for cognitive training. A quality model based on the ISO 25010 standard is presented with an evaluation method that references the ISO 25040 standard. The quality model focuses on Functional Suitability, while the evaluation method focuses on the Running the Assessment phase. The inputs generated were tested in an experimental environment. The results show that an effective, efficient, and satisfactory evaluation of DSLs within the domain can be developed from the generated quality and evaluation methods.