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Titlebook: Question Answering over Text and Knowledge Base; Saeedeh Momtazi,Zahra Abbasiantaeb Book 2022 The Editor(s) (if applicable) and The Author

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發(fā)表于 2025-3-21 19:44:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Question Answering over Text and Knowledge Base
編輯Saeedeh Momtazi,Zahra Abbasiantaeb
視頻videohttp://file.papertrans.cn/782/781812/781812.mp4
概述Provides a comprehensive overview on QA systems over text (TextQA), over knowledge base (KBQA), and hybrid ones.Explains state-of-the-art models used in real applications of QA systems and discusses f
圖書封面Titlebook: Question Answering over Text and Knowledge Base;  Saeedeh Momtazi,Zahra Abbasiantaeb Book 2022 The Editor(s) (if applicable) and The Author
描述.This book provides a coherent and complete overview of various Question Answering (QA) systems. It covers three main categories based on the source of the data that can be unstructured text (TextQA), structured knowledge graphs (KBQA), and the combination of both. Developing a QA system usually requires using a combination of various important techniques, including natural language processing, information retrieval and extraction, knowledge graph processing, and machine learning...After a general introduction and an overview of the book in Chapter 1, the history of QA systems and the architecture of different QA approaches are explained in Chapter 2. It starts with early close domain QA systems and reviews different generations of QA up to state-of-the-art hybrid models. Next, Chapter 3 is devoted to explaining the datasets and the metrics used for evaluating TextQA and KBQA. Chapter 4 introduces the neural and deep learning models used in QA systems. This chapter includes the required knowledge of deep learning and neural text representation models for comprehending the QA models over text and QA models over knowledge base explained in Chapters 5 and 6, respectively. In some of t
出版日期Book 2022
關(guān)鍵詞Information Retrieval; Natural Language Processing; Neural Networks; Deep Learning; Artificial Intellige
版次1
doihttps://doi.org/10.1007/978-3-031-16552-8
isbn_softcover978-3-031-16554-2
isbn_ebook978-3-031-16552-8
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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沙發(fā)
發(fā)表于 2025-3-21 20:37:25 | 只看該作者
Introduction to Neural Networks,n this chapter. We study the neural architectures widely used in Sect. 2.3.2 and avoid repeating some of the details described in this section in the following chapters. We also describe the available word representation model from traditional and state-of-the-art models that are utilized in QA systems.
板凳
發(fā)表于 2025-3-22 02:26:19 | 只看該作者
Question Answering over Knowledge Base,ach category, various researches will be presented by discussing their architecture. This chapter includes a comprehensive comparison of proposed methods and describes state-of-the-art models in terms of both simple and complex QA.
地板
發(fā)表于 2025-3-22 07:45:18 | 只看該作者
5#
發(fā)表于 2025-3-22 08:46:31 | 只看該作者
Question Answering in Real Applications,ll discuss the architecture of these full pipeline QA approaches as well as their applications. Considering the advantages of both textual data and knowledge bases, the real applications of QA aim to benefit from both sources. Therefore, in this chapter, we will see how a combination of both approaches can be used in real scenarios.
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發(fā)表于 2025-3-22 16:30:19 | 只看該作者
Saeedeh Momtazi,Zahra AbbasiantaebProvides a comprehensive overview on QA systems over text (TextQA), over knowledge base (KBQA), and hybrid ones.Explains state-of-the-art models used in real applications of QA systems and discusses f
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https://doi.org/10.1007/978-3-031-16552-8Information Retrieval; Natural Language Processing; Neural Networks; Deep Learning; Artificial Intellige
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發(fā)表于 2025-3-23 03:07:24 | 只看該作者
978-3-031-16554-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-23 05:54:59 | 只看該作者
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