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Machine learning for text /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Machine learning for text // Charu C. Aggarwal.
作者:	Aggarwal, Charu C.
出版者:	Cham, Switzerland :Springer,2022.
面頁冊數:	xxiii, 565 p. :ill. (some col.) ;26 cm.
標題:	Machine learning. -
ISBN:	9783030966225
ISBN:	3030966224

Machine learning for text /
Aggarwal, Charu C.

Machine learning for text /Charu C. Aggarwal. - 2nd ed. - Cham, Switzerland :Springer,2022. - xxiii, 565 p. :ill. (some col.) ;26 cm.

Includes bibliographical references and index.

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: 9783030966225NT2361Subjects--Topical Terms:

147118
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.3/1

Machine learning for text /
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100 1 $a Aggarwal, Charu C. $3 1000148850
245 1 0 $a Machine learning for text / $c Charu C. Aggarwal.
250 $a 2nd ed.
260 $a Cham, Switzerland : $b Springer, $c 2022.
300 $a xxiii, 565 p. : $b ill. (some col.) ; $c 26 cm.
504 $a Includes bibliographical references and index.
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 147118
650 0 $a Data mining. $3 147024