This second edition textbook covers a coherently organized
framework for text analytics, which integrates material drawn
from the intersecting topics of information retrieval, machine
learning, and natural language processing. Particular
importance is placed on deep learning methods. The chapters
of this book span three broad categories:1. Basic algorithms:
Chapters 1 through 7 discuss the classical algorithms for text
analytics such as preprocessing, similarity computation, topic
modeling, matrix factorization, clustering, classification,
regression, and ensemble analysis. 2. Domain-sensitive learning and
information retrieval: Chapters 8 and 9 discuss learning models in
heterogeneous settings such as a combination of text with
multimedia or Web links. The problem of information retrieval and
Web search is also discussed in the context of its relationship
with ranking and machine learning methods. 3. Natural
language processing: Chapters 10 through 16 discuss various
sequence-centric and natural language applications, such as feature
engineering, neural language models, deep learning, transformers,
pre-trained language models, text summarization, information
extraction, knowledge graphs, question answering, opinion mining,
text segmentation, and event detection. Compared to the
first edition, this second edition textbook (which targets mostly
advanced level students majoring in computer science and math) has
substantially more material on deep learning and natural
language processing. Significant focus is placed on topics
like transformers, pre-trained language models, knowledge
graphs, and question answering.
General
Imprint: |
Springer Nature Switzerland AG
|
Country of origin: |
Switzerland |
Release date: |
May 2023 |
First published: |
2022 |
Authors: |
Charu C. Aggarwal
|
Dimensions: |
254 x 178mm (L x W) |
Pages: |
565 |
Edition: |
2nd ed. 2022 |
ISBN-13: |
978-3-03-096625-6 |
Categories: |
Books
|
LSN: |
3-03-096625-9 |
Barcode: |
9783030966256 |
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