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Machine Learning for Text

  • Charu C. Aggarwal

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Charu C. Aggarwal
    Pages 1-16
  3. Charu C. Aggarwal
    Pages 17-30
  4. Charu C. Aggarwal
    Pages 31-72
  5. Charu C. Aggarwal
    Pages 73-112
  6. Charu C. Aggarwal
    Pages 113-157
  7. Charu C. Aggarwal
    Pages 159-207
  8. Charu C. Aggarwal
    Pages 209-234
  9. Charu C. Aggarwal
    Pages 235-258
  10. Charu C. Aggarwal
    Pages 259-304
  11. Charu C. Aggarwal
    Pages 305-360
  12. Charu C. Aggarwal
    Pages 361-380
  13. Charu C. Aggarwal
    Pages 381-411
  14. Charu C. Aggarwal
    Pages 413-434
  15. Charu C. Aggarwal
    Pages 435-452
  16. Back Matter
    Pages 453-493

About this book

Introduction

Text analytics is a field that lies on the interface of information retrieval, machine learning,

and natural language processing. This book carefully covers a coherently organized framework

drawn from these intersecting topics. The chapters of this book span three broad categories:

 

1. Basic algorithms: Chapters 1 through 8 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: 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. 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 book covers text analytics and machine learning topics from the simple to the advanced.

Since the coverage is extensive, multiple courses can be offered from the same book,

depending on course level.

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

Authors and affiliations

  • Charu C. Aggarwal
    • 1
  1. 1.IBM T. J. Watson Research CenterYorktown HeightsUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-73531-3
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-73530-6
  • Online ISBN 978-3-319-73531-3
  • Buy this book on publisher's site