Machine Learning for Text / by Charu C. Aggarwal.

Format:

Book

Author/Creator:

Aggarwal, Charu C., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Machine learning.

Data mining.

Information storage and retrieval systems.

Machine Learning.

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Local Subjects:

Machine Learning.

Data Mining and Knowledge Discovery.

Information Storage and Retrieval.

Physical Description:

1 online resource (XXIII, 565 pages) : 92 illustrations, 5 illustrations in color.

Edition:

2nd ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

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 Language Modeling and Deep Learning

11 Attention Mechanisms and Transformers

12 Text Summarization

13 Information Extraction and Knowledge Graphs

14 Question Answering

15 Opinion Mining and Sentiment Analysis

16 Text Segmentation and Event Detection.

Other Format:

Printed edition:

ISBN:

978-3-030-96623-2

9783030966232

Access Restriction:

Restricted for use by site license.