Examinando por Materia "Procesamiento de lenguaje natural"
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Ítem Análisis de discurso basado en modelos grandes de lenguaje(Universidad EAFIT, 2024) Jiménez Jaimes, Edgar Leandro; Montoya Múnera, Edwin NelsonThis thesis explores the implementation of natural language processing techniques and large language models (LLMs) to support discourse analysis tasks in the context of the "Tenemos que hablar Colombia" program. Techniques such as topic modeling, sentiment analysis, clustering, visualization, and the creation of a conversational assistant based on Retrieval Augmented Generation (RAG) have been addressed using advanced text modeling, vector embeddings, and prompt engineering approaches. A text classification model focused on predicting the label of the verbal indicator variable, assigned manually by the interviewer, is also presented, although this model is not directly applied to discourse analysis. This work adds to the studies of the " Tenemos que hablar Colombia " program, where other authors have contributed through computational linguistics analysis and machine learning techniques. Using advanced NLP techniques, we have sought to improve the interpretation of text data and its application in discourse analysis. The results have shown improvements in the accuracy of data classification and analysis through the techniques explored, providing a better understanding of citizen perceptions.Ítem Análisis de la tendencia de la solución de una interacción con un Chatbot de atención al cliente, basado en análisis de sentimiento y otras variables(Universidad EAFIT, 2023) Flórez Salazar, Luz Stella; Montoya Múnera, Edwin NelsonA chatbot is a program created with artificial intelligence. In the context of customer service, can establish conversations with customers and they are trained to resolve their queries, problems and complaints. A chatbot’s skill to identify when a customer is not meeting their request represents a challenge for companies that currently use this technology. One of the strategies to avoid quitting the conversation for this reason, is to escalate or transfer the conversation to a human agent. Therefore, it is essential to detect when it is time to carry out this escalation. This project evaluates different Natural Language Processing (NLP) techniques, rule-based labeling algorithms, classical supervised machine learning models and a simple neural network for classification, applied to interactions between a customer service chatbot and a user, in order to find a mechanism for automatic labeling of the data and to build a model that can be used to make the decision on whether the customer should continue interacting with the chatbot or if he should be transferred to a conversation with a human agent. The labeling mechanism could also be used to classify historical data, to later train a model. Different models and techniques are evaluated and those with the best performance in detecting the conversations that should escalate to a human agent are presented.Ítem Análisis de los resultados de la aplicación del instrumento para la evaluación docente de la universidad EAFIT(Universidad EAFIT, 2024) Fernández Carmona, Laura Catalina; Guarín Zapata, Nicolás; Mola Ávila, José AntonioÍtem Análisis de registros de mantenimiento de centrales de generación de energía con técnicas de procesamiento de lenguaje natural(Universidad EAFIT, 2024) Ocampo Davila, Andrés Alonso; Salazar Martínez, Carlos AndresÍtem Definición de una metodología para análisis de discurso basado en lingüística computacional y técnicas de aprendizaje de máquina(Universidad EAFIT, 2023) Fajardo Becerra, Daian Paola; Montoya Múnera, Edwin Nelson; Ariza Jiménez, Leandro FabioThe different actions carried out by a state regulatory body generate multiple opinions among citizens, which form debates among people, causing them to agree, disagree or partially agree with the decisions or strategies proposed. In order to know the opinions of the citizens, in Chile a project called "Tenemos que hablar Chile" (We have to talk Chile) was created, which asked structured questions to a group of citizens, where the answer of each person was classified by the moderator. each person's answer was classified by the moderator. This label was used for different discourse analyses that began to be developed without any specific order. This project was replicated in Colombia, under the same dynamics in order to know the opinions of the citizens, however, the techniques used were different from the Chilean project. As a result, it is observed that although both projects had the same dynamics and sought a similar result, it was not possible to reuse the techniques developed in the Chilean project in Colombia. Due to this, the proposal of this master's project seeks the implementation of a methodology that allows the use of different techniques of discourse analysis based on computational linguistics and machine learning that will provide the team of analysts with a scheme of stages which will have tools and techniques of Natural Language processing (NLP) to improve the efficiency of this type of projects. Within this project we can highlight the strengths of the director who has a high experience in Machine Learning (ML) and NLP, in addition to the strengths of the co-director with a broad understanding of the project "Tenemos que Hablar Colombia" (TQHC), and finally the student of this project with a base in the Master of Data Science and Analytics to generate a research on NLP techniques.Ítem Predicción de incumplimiento de pagos de crédito en una entidad financiera utilizando chats de servicio al cliente(Universidad EAFIT, 2023) Patiño Serna, Javier; Martínez Vargas, Juan David; Vallejo Correa, Paola Andrea