Machine Learning for Text / by Charu C. Aggarwal.

Format:

Book

Author/Creator:

Aggarwal, Charu C., author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Physical Description:

1 online resource (XXIII, 493 pages) : 80 illustrations, 4 illustrations in color

Edition:

First edition 2018.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

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.

Contents:

1 An Introduction to Text Analytics

2 Text Preparation and Similarity Computation

3 Matrix Factorization and Topic Modeling

4 Text Clustering

5 Text Classification: Basic Models

6 Linear Models for Classification and Regression

7 Classifier Performance and Evaluation

8 Joint Text Mining with Heterogeneous Data

9 Information Retrieval and Search Engines

10 Text Sequence Modeling and Deep Learning

11 Text Summarization

12 Information Extraction

13 Opinion Mining and Sentiment Analysis

14 Text Segmentation and Event Detection.

Other Format:

Printed edition:

ISBN:

978-3-319-73531-3

9783319735313

9783319735306

9783319735320

9783030088071

Access Restriction:

Restricted for use by site license.