|
Showing 1 - 1 of
1 matches in All Departments
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 the
KBQA models the textual data is also used as another source besides
the knowledge base; these hybrid models are studied in Chapter 7.
In Chapter 8, a detailed explanation of some well-known real
applications of the QA systems is provided. Eventually, open issues
and future work on QA are discussed in Chapter 9. This book
delivers a comprehensive overview on QA over text, QA over
knowledge base, and hybrid QA systems which can be used by
researchers starting in this field. It will help its readers to
follow the state-of-the-art research in the area by providing
essential and basic knowledge.
|
You may like...
Ab Wheel
R209
R149
Discovery Miles 1 490
|
Email address subscribed successfully.
A activation email has been sent to you.
Please click the link in that email to activate your subscription.