Natural Language Analytics with Generative Large-Language Models: A Practical Approach with Ollama and Open-Source LLMs (SpringerBriefs in Computer Science) - Marcondes, Francisco S.; Gala, Adelino; Magalh�es, Renata; Perez De Britto, Fernando; Dur�es, Dalila; Novais, Paulo: 9783031766305

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Natural Language Analytics with Generative Large-Language Models (Paperback)

Francisco S. Marcondes

Published by Springer International Publishing AG, Cham, 2025

ISBN 10: 303176630X ISBN 13: 9783031766305

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Paperback. Condition: new. Paperback. 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 dataqualitative 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. This book explores the application of generative Large Language Models (LLMs) for extracting and analyzing data from natural language artefacts. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783031766305

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Natural Language Analytics with Generative Large-Language Models

Francisco S. Marcondes

Published by Springer, Berlin, Springer Nature Switzerland, Springer, 2025

ISBN 10: 303176630X ISBN 13: 9783031766305

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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 84 pp. Englisch. Seller Inventory # 9783031766305

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Natural Language Analytics with Generative Large-Language Models : A Practical Approach with Ollama and Open-Source LLMs

Francisco S. Marcondes

Published by Springer Nature Switzerland, Springer International Publishing, 2025

ISBN 10: 303176630X ISBN 13: 9783031766305

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783031766305

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