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Machine learning for text

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for text/ by Charu C. Aggarwal.
Author:	Aggarwal, Charu C.
Published:	Cham :Springer International Publishing : : 2022.,
Description:	xxiii, 565 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-96623-2
ISBN:	9783030966232

Machine learning for text
Aggarwal, Charu C.

Machine learning for text[electronic resource] /by Charu C. Aggarwal. - Second edition. - Cham :Springer International Publishing :2022. - xxiii, 565 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783030966232

Standard No.: 10.1007/978-3-030-96623-2doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for text
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245 1 0 $a Machine learning for text $h [electronic resource] / $c by Charu C. Aggarwal.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xxiii, 565 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Data mining. $3 562972
650 1 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Information Storage and Retrieval. $3 761906
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