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Machine Learning for Text

  • Textbook
  • © 2022
  • Latest edition

Overview

Authors:
  1. Charu C. Aggarwal

    1. Mohegan Lake, USA

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  • Integrates treatment of text mining/learning, information retrieval and natural language processing
  • Has a strong focus on deep learning, transformers and pre-trained language models
  • Simplifies the mathematical presentation with intuitive explanations
  • Request lecturer material: sn.pub/lecturer-material

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  2. An Introduction to Text Analytics

    • Charu C. Aggarwal
    Pages 1-17

  3. Text Preparation and Similarity Computation

    • Charu C. Aggarwal
    Pages 19-32

  4. Matrix Factorization and Topic Modeling

    • Charu C. Aggarwal
    Pages 33-74

  5. Text Clustering

    • Charu C. Aggarwal
    Pages 75-114

  6. Text Classification: Basic Models

    • Charu C. Aggarwal
    Pages 115-158

  7. Linear Models for Classification and Regression

    • Charu C. Aggarwal
    Pages 159-206

  8. Classifier Performance and Evaluation

    • Charu C. Aggarwal
    Pages 207-232

  9. Joint Text Mining with Heterogeneous Data

    • Charu C. Aggarwal
    Pages 233-256

  10. Information Retrieval and Search Engines

    • Charu C. Aggarwal
    Pages 257-302

  11. Language Modeling and Deep Learning

    • Charu C. Aggarwal
    Pages 303-368

  12. Attention Mechanisms and Transformers

    • Charu C. Aggarwal
    Pages 369-391

  13. Text Summarization

    • Charu C. Aggarwal
    Pages 393-418

  14. Information Extraction and Knowledge Graphs

    • Charu C. Aggarwal
    Pages 419-463

  15. Question Answering

    • Charu C. Aggarwal
    Pages 465-489

  16. Opinion Mining and Sentiment Analysis

    • Charu C. Aggarwal
    Pages 491-514

  17. Text Segmentation and Event Detection

    • Charu C. Aggarwal
    Pages 515-532

  18. Correction to: Machine Learning for Text

    • Charu C. Aggarwal
    Pages C1-C1

  19. Back Matter

    Pages 533-565
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Keywords

About this book

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.



Authors and Affiliations

  • Mohegan Lake, USA

    Charu C. Aggarwal

About the author

Charu C. Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his undergraduate degree in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his Ph.D. in Operations Research from the Massachusetts Institute of Technology in 1996. He has published more than 400 papers in refereed conferences and journals, and has applied for or been granted more than 80 patents. He is author or editor of 20 books, including textbooks on linear algebra, machine learning (for text), neural networks, recommender systems, and outlier analysis. Because of the commercial value of his patents, he has thrice been designated a Master Inventor at IBM. He has received several internal and external awards, including the EDBT Test-of-Time Award (2014), the ACM SIGKDD Innovation Award (2019), and the IEEE ICDM Research Contributions Award (2015). He is also a recipient of the W. Wallace McDowell Award, which is the highest technical honor given by IEEE Computer Society in the field of computer science. He has served as an editor-in-chief of the ACM SIGKDD Explorations. He is currently serving as the editor-in-chief of the ACM Transactions on Knowledge Discovery from Data and as an editor-in-chief of ACM Books. He is a fellow of the SIAM, ACM, and the IEEE, for “contributions to knowledge discovery and data mining algorithms.”

Bibliographic Information

  • Book Title: Machine Learning for Text

  • Authors: Charu C. Aggarwal

  • DOI: https://doi.org/10.1007/978-3-030-96623-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2022

  • Hardcover ISBN: 978-3-030-96622-5Published: 05 May 2022

  • Softcover ISBN: 978-3-030-96625-6Published: 06 May 2023

  • eBook ISBN: 978-3-030-96623-2Published: 04 May 2022

  • Edition Number: 2

  • Number of Pages: XXIII, 565

  • Number of Illustrations: 87 b/w illustrations, 5 illustrations in colour

  • Topics: Machine Learning, Data Mining and Knowledge Discovery, Information Storage and Retrieval

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