| 书目名称 | Question Answering over Text and Knowledge Base | | 编辑 | Saeedeh Momtazi,Zahra Abbasiantaeb | | 视频video | | | 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 | | 图书封面 |  | | 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 | | 关键词 | Information Retrieval; Natural Language Processing; Neural Networks; Deep Learning; Artificial Intellige | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-16552-8 | | isbn_softcover | 978-3-031-16554-2 | | isbn_ebook | 978-3-031-16552-8 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 19:44:34
