Overview
- The first textbook to cover machine learning of text in a holistic way, which includes aspects of mining, language modeling, and deep learning
- Includes many examples to simplify exposition and facilitate in learning. Semantically understandable illustrations are provided, so that they can be used in classroom teaching
- Provides comprehensive coverage of this field.The depth and breadth of coverage
- is unique to this textbook
- Request lecturer material: sn.pub/lecturer-material
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (14 chapters)
-
Front Matter
-
Back Matter
Keywords
- Data mining
- Text mining
- Information retrieval
- Text clustering
- Dimensionality reduction
- Matrix factorization
- Text classification
- Mining text data
- text analytics
- natural language processing
- machine learning
- deep learning
- information extraction
- opinion mining
- word2vec
- recurrent neural network
- search engines
About this book
Text analytics is a field that lies on the interface of information retrieval,machine learning, and natural language processing, and this textbook carefully covers a coherently organized framework drawn from these intersecting topics. The chapters of this textbook is organized into three categories:
- Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for machine learning from text such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis.
- Domain-sensitive mining: Chapters 8 and 9 discuss the learning methods from text when combined with different domains such as multimedia and the Web. The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods.
- Sequence-centric mining: Chapters 10 through 14 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, text summarization, information extraction, opinion mining, text segmentation, and event detection.
This textbook covers machine learning topics for text in detail. Since the coverage is extensive,multiple courses can be offered from the same book, depending on course level. Even though the presentation is text-centric, Chapters 3 to 7 cover machine learning algorithms that are often used indomains beyond text data. Therefore, the book can be used to offer courses not just in text analytics but also from the broader perspective of machine learning (with text as a backdrop).
This textbook targets graduate students in computer science, as well as researchers, professors, and industrial practitioners working in these related fields. This textbook is accompanied with a solution manual for classroom teaching.
Reviews
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Machine Learning for Text
Authors: Charu C. Aggarwal
DOI: https://doi.org/10.1007/978-3-319-73531-3
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-73530-6Published: 03 April 2018
Softcover ISBN: 978-3-030-08807-1Published: 01 February 2019
eBook ISBN: 978-3-319-73531-3Published: 19 March 2018
Edition Number: 1
Number of Pages: XXIII, 493
Number of Illustrations: 76 b/w illustrations, 4 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Artificial Intelligence