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This book explores the application of generative Large Language Models (LLMs) for extracting and analyzing data from natural language artefacts. Unlike traditional uses of LLMs, such as translation and summarization, this book focuses on utilizing these models to convert unstructured text into data that can be processed through the data science pipeline to generate actionable insights.
The content is designed for professionals in diverse fields including cognitive science, linguistics, management, and information systems. It combines insights from both industry and academia to provide a comprehensive understanding of how LLMs can be effectively used for natural language analytics (NLA). The book details practical methodologies for implementing LLMs locally using open-source tools, ensuring data privacy and feasibility without the need for expensive infrastructure.
Key topics include interpretant, mindset and cultural analysis, emphasizing the use of LLMs to derive soft data-qualitative information crucial for nuanced decision-making. The text also outlines the technical aspects of LLMs, including their architecture, token embeddings, and the differences between encoder-based and decoder-based models. By providing a case study and practical examples, the authors show how LLMs can be used to meet various analytical needs, making this book a valuable resource for anyone looking to integrate advanced natural language processing techniques into their data analysis workflows.
Francisco S. Marcondes is a research collaborator at the Synthetic Intelligence Laboratory (ISLab) at the ALGORITMI Centre at the University of Minho (Braga, Portugal). He has teaching experience in Brazil (including the Pontifical Catholic University of São Paulo) and Portugal (including the University of Minho). He has a degree in Information Systems (2004), a master's degree in Intelligence Technologies and Digital Design (2015) and specialised in Pedagogical Training for Professional Education (2017). He is currently studying for a PhD in Artificial Intelligence and Natural Language Processing.
Adelino Gala is a researcher specializing in Natural Language Processing (NLP), Cognitive Science, Semiotics, and the application of Large and Small Language Models. He holds a Ph.D. in Technologies of Intelligence and Digital Design from the Pontifical Catholic University of São Paulo, Brazil, where his work emphasized learning and cognitive semiotics, and completed his postdoctoral studies at the University of Aveiro, Portugal, focusing on the integration of new digital technologies in communication practices.
Renata Magalhães is a PhD student in Biomedical Engineering at the University of Minho, where she also completed her master's degree (2024) in Digital Humanities. Her thesis explored the topic of emotion detection for school failure prevention. Her research focuses on Natural Language Processing and Artificial Intelligence.
Fernando Perez de Britto has extensive experience in analytics, artificial intelligence and decision support solutions for private organizations, with a master's degree in the area. Reference in the UN concept of Coherence. Founder and CEO of Investment 4 Impact, an investment holding focused on innovation, high technology and impact originating from AI Systems Research (AISR), founded in 2002. He is also responsible for the holding's socio-environmental initiative: "Making Smart Cities". Co-Chair Emeritus of the UNDRR Stakeholder Engagement Mechanism (SEM). Former Vice-Chair of the Global Council of the UNDRR Private Sector Alliance for Disaster Resilient Societies (ARISE) (2020-2023), led the ARISE delegation in the Mid-Term Review of the Implementation of the Sendai Framework for Disaster Risk Reduction 2015- 2030 held in NY (2023), UNDRR ARISE Global Board Member (2017-2023), UNDRR Stakeholder Engagement Mechanism (SEM) Co-Chair (2019-2021) and UNDRR ARISE Advisory Board Member (2015-2017). He received the UN Sasakawa Prize in 2019. Co-author of the book "Smart Cities: why, for whom?" (2016). Master in Intelligence Technologies and Digital Design (Cognitive Sciences) from the Pontifical Catholic University of São Paulo (PUC/SP), specialization in Business and Administration from Fundação Getúlio Vargas de São Paulo (FGV-EAESP) and bachelor's degree in Computer Science from Pontifícia Catholic University of São Paulo (PUC/SP).
Dalila Durães is Assistant Professor of Computer Sciences at the Department of Informatics, University of Minho, Braga, Portugal. Researcher at the ALGORITMI Centre and LASI (Intelligent Systems Associate Laboratory) at the group ISlab - Synthetic Intelligence. She graduated in Electronic Engineering and Informatics in 1995 and, in 2004, completed her Master's Degree in Industrial Electronic Engineering in Automation and Robotics. She holds two international PhDs: one in Educational Sciences, in Teachers, Curricula and Educational Institutions, from the University of Granada, Spain, completed in 2012, and another in Artificial Intelligence by the Polytechnic University of Madrid, Spain, completed in 2018. She started her career on 2015 developing scientific research in the field of Intelligent Systems/Artificial Intelligence (AI), namely in Human-Computer Interaction, Machine and Deep Learning, Behaviour Analysis, with particular attention to the detection of violence, sentiment analysis, intelligent tutoring and recognition of human actions. Her interests, in the last years, were absorbed by the broad, yet closely related, concepts of Intelligent Environments, Data Fusion, Digital Assistants, Artificial Intelligent applying to Education, and the incorporation of AI methods and techniques in these fields. Its main research objective is to make systems smarter, sensible, and reliable. She is the author and co-author of over 80 book chapters, journal papers, conference and workshop papers, and books (with peer review). She is also a member of the editorial board of several international journals. During the past few years, she has served as an expert/reviewer for several conferences and journals. She has participated in several research projects sponsored by Portuguese and European public and private Institutions. She has supervised several Ph.D. and 15 M.Sc. students. She is Vice-chair of the Portuguese Association for Artificial Intelligence (APPIA) and Vice-chair of IEEE, Computational Intelligence Society, Portuguese Chapter. She has 2 Awards and Distinctions - Recognition by the Scientific Community: Best Paper in 2nd International Conference on sustainable Smart Cities and Territories International 2023, and Best Paper Application Award in 16th International Conference on Distributed Computing and Artificial Intelligence 2019. International research collaboration with different universities, resulting on the production several papers for journals with high impact factor, several conferences with peer-reviews and some book chapter.
Paulo Novais is Full Professor in the Department of Computer Science and researcher at the ALGORITMI Centre, in the School of Engineering at the University of Minho. Coordinator of the Associate Laboratory of Intelligent Systems (LASI). His main research goal is to design systems that are more sensitive to human presence, a little smarter and more reliable. He is Director of the PhD Programme in Informatics, co-founder and Deputy Director of the Master's Degree in Law and Informatics at the University of Minho and served as President of the Portuguese Association for Artificial Intelligence (APPIA) between 2016 and 2019 and current Chairman of the Supervisory Board. Portuguese representative at IFIP (International Federation for Information Processing) - Artificial Intelligence Technical Group. He is chair of the Computational Intelligence Society Portuguese Chapter and Senior member of the IEEE (Institute of Electrical and Electronics Engineers), a member of the executive committee of IBERAMIA (IberoAmerican Society of Artificial Intelligence) and has worked as an expert for various institutions such as the European Commission, FCT, A3ES, ANI and the Calouste Gulbenkian Foundation, among others.
Introduction.- Natural Language Analytics.- Using Ollama.- Generative Prompt Engineering.- Case Study: LLM-based Anxiety Climate Index.- Conclusion.
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