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Machine Learning for Text - Research Book

Machine Learning for Text - Research Book

Interesting Research Book in Machine Learning for Text

Author(s) Name:  Charu C. Aggarwal

About the Book:

   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: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 andnatural language applications, such as feature engineering, neural language models,deep learning, text summarization, information extraction, opinion mining, text segmentation,and event detection.

Table of Contents

  • Machine Learning for Text: An Introduction
  • Text Preparation and Similarity Computation
  • Matrix Factorization and Topic Modeling
  • Text Clustering
  • Text Classification: Basic Models
  • Linear Classification and Regression for Text
  • Classifier Performance and Evaluation
  • Joint Text Mining with Heterogeneous Data
  • Information Retrieval and Search Engines
  • Text Sequence Modeling and Deep Learning
  • Opinion Mining and Sentiment Analysis
  • Text Segmentation and Event Detection
  • ISBN:  978-3-319-73531-3

    Publisher:   Springer Publisher

    Year of Publication:  2018

    Book Link:  Home Page Url