Francisco S. Marcondes, Adelino Gala, Renata Magalhães

Natural Language Analytics with Generative Large-Language Models

A Practical Approach with Ollama and Open-Source LLMs

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• SpringerBriefs in Computer Science
• Verlag: Springer / Springer Nature Switzerland / Springer, Berlin
• Artikelnr. des Verlages: 978-3-031-76630-5
• Seitenzahl: 100
• Erscheinungstermin: 14. Februar 2025
• Englisch
• Abmessung: 235mm x 155mm x 6mm
• Gewicht: 166g
• ISBN-13: 9783031766305
• ISBN-10: 303176630X
• Artikelnr.: 71783066

Autorenporträt

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 int

Inhaltsangabe

Introduction.- Natural Language Analytics.- Using Ollama.- Generative Prompt Engineering.- Case Study: LLM-based Anxiety Climate Index.- Conclusion.