Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- Domain-adapted pretraining and topic modeling for identifying skills categories in job postings(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-12-05) Madera Espíndola, Diana Patricia; Ceballos Cancino, Héctor Gibrán; Vázquez Lepe, Elisa Virginia; mtyahinojosa, emipsanchez; González Gómez, Luis José; Fahim Siddiqui, Muhammad Hammad; Cantú Ortiz, Francisco Javier; Escuela de Ingeniería y Ciencias; Campus Estado de México; Butt, SaburThe need to identify and cluster related skills in job postings has become increasingly essential as the labor market becomes more complex, driven by the rapid growth in job market data and continuous shifts in economic conditions, technology, and skill requirements. This task is especially challenging for postings in low-resources languages such as Spanish, as there is a lack of models specifically trained to handle these language variations. Previous work in this regard involves taxonomies created by experts such as ESCO, intended to be used as reference points via measured skills. However, some issues associated with these systems stem from their reliance on region-specific taxonomies as well as their rigidity to adapt to the changing environment of the market. Thus, we proposed a method to improve skill identification performance within the Mexican automotive industry by grouping equivalent skills present in Spanish job postings through the integration of text normalization, a Domain-Adaptive Pre-training (DAPT) Spanish BERT model, the use of BERTopic for pseudo-labels extraction, the improvement of vocabulary representation via VGCN embeddings, and similarity metrics such as keyword overlap and cosine similarity for final refined clustering. The scope of this research is to evaluate our approach by using an Adjusted Rand Index (ARI) score in skill classification on a dataset exhibiting a long-tail distribution across both the head and tail data, comparing the results to those of an initial Non-DAPT model, since, to the best of our knowledge, no direct approach exists that is comparable to either our ensemble model or the distribution of our dataset.
- Aspect based sentiment analysis in students’ evaluation of teaching(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05) Acosta Ugalde, Diego; Conant Pablos, Santiago Enrique; mtyahinojosa, emipsanchez; Guitérrez Rodríguez, Andrés Eduardo; Juárez Jiménez, Julio Antonio; Morales Méndez, Rubén; School of Engineering and Sciences; Campus Monterrey; Camacho Zuñiga, ClaudiaStudent evaluations of teachings (SETs) are essential for assessing educational quality. Natural Language Processing (NLP) techniques can produce informative insights from these evaluations. The large quantity of text data received from SETs has surpassed the capacity for manual processing. Employing NLP to analyze student feedback offers an efficient method for understanding educational experiences, enabling educational institutions to identify patterns and trends that might have been difficult, if not impossible, to notice with a manual analysis. Data mining using NLP techniques can delve into the thoughts and perspectives of students on their educational experiences, identifying sentiments and aspects that may have a level of abstraction that the human analysis cannot perceive. I use different NLP techniques to enhance the analysis of student feedback in the form of comments and provide better insights and understanding into factors that influence students’ sentiments. This study aims to provide an overview of the various approaches used in NLP and sentiment analysis, focusing on analyzing the models and text representations used to classify numerical scores obtained from the text feedback of a corpus of SETs in Spanish. I provide a series of experiments using different text classification algorithms for sentiment classification over numerical scores of educational aspects. Additionally, I explore two Aspect Based Sentiment Analysis (ABSA) models, a pipeline and a multi-task approach, to extract broad and comprehensive insights from educational feedback for each professor. The results of this research demonstrate the effectiveness of using NLP techniques for analyzing student feedback. The sentiment classification experiments showed favorable outcomes, indicating that it is possible to utilize student comments to classify certain educational scores accurately. Furthermore, the qualitative results obtained from the ABSA models, presented in a user-friendly dashboard, highlight the efficiency and utility of employing these algorithms for the analysis of student feedback. The dashboard provides valuable insights into the sentiments expressed by students regarding various aspects of their educational experience, allowing for a more comprehensive understanding of the factors influencing their opinions. These findings highlight the potential of NLP in the educational domain, offering a powerful tool for institutions to gain a deeper understanding of student perspectives and make data-driven decisions to enhance the quality of education.