國立虎尾科技大學 |

Machine Learning for Text

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine Learning for Text/ by Charu C. Aggarwal.
Author:	Aggarwal, Charu C.
Description:	XXIII, 493 p. 80 illus., 4 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-3-319-73531-3
ISBN:	9783319735313

Machine Learning for Text
Aggarwal, Charu C.

Machine Learning for Text[electronic resource] /by Charu C. Aggarwal. - 1st ed. 2018. - XXIII, 493 p. 80 illus., 4 illus. in color.online resource.

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.

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.

ISBN: 9783319735313

Standard No.: 10.1007/978-3-319-73531-3doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Machine Learning for Text
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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 Text Sequence Modeling and Deep Learning -- 11 Text Summarization -- 12 Information Extraction -- 13 Opinion Mining and Sentiment Analysis -- 14 Text Segmentation and Event Detection.
520 $a 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.
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