國立虎尾科技大學 |

Text Data Mining

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text Data Mining/ by Chengqing Zong, Rui Xia, Jiajun Zhang.
Author:	Zong, Chengqing.
other author:	Xia, Rui.
Description:	XXI, 351 p. 214 illus., 7 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Natural language processing (Computer science). -
Online resource:	https://doi.org/10.1007/978-981-16-0100-2
ISBN:	9789811601002

Text Data Mining
Zong, Chengqing.

Text Data Mining[electronic resource] /by Chengqing Zong, Rui Xia, Jiajun Zhang. - 1st ed. 2021. - XXI, 351 p. 214 illus., 7 illus. in color.online resource.

Chapter 1. Introduction -- Chapter 2. Data Annotation and Preprocessing -- Chapter 3. Text Representation -- Chapter 4. Text Representation with Pretraining and Fine-tuning -- Chapter 5. Text classification -- Chapter 6. Text Clustering -- Chapter 7. Topic Model -- Chapter 8. Sentiment Analysis and Opinion Mining -- Chapter 9. Topic Detection and Tracking -- Chapter 10. Information Extraction -- Chapter 11. Automatic Text Summarization. .

This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.

ISBN: 9789811601002

Standard No.: 10.1007/978-981-16-0100-2doiSubjects--Topical Terms:

802180
Natural language processing (Computer science).

LC Class. No.: QA76.9.N38

Dewey Class. No.: 006.35

Text Data Mining
LDR:02800nam a22003975i 4500 001 1054682
003 DE-He213
005 20210621122913.0
007 cr nn 008mamaa
008 220103s2021 si | s |||| 0|eng d
020 $a 9789811601002 $9 978-981-16-0100-2
024 7 $a 10.1007/978-981-16-0100-2 $2 doi
035 $a 978-981-16-0100-2
050 4 $a QA76.9.N38
072 7 $a UYQL $2 bicssc
072 7 $a COM073000 $2 bisacsh
072 7 $a UYQL $2 thema
082 0 4 $a 006.35 $2 23
100 1 $a Zong, Chengqing. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1359799
245 1 0 $a Text Data Mining $h [electronic resource] / $c by Chengqing Zong, Rui Xia, Jiajun Zhang.
250 $a 1st ed. 2021.
264 1 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2021.
300 $a XXI, 351 p. 214 illus., 7 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1. Introduction -- Chapter 2. Data Annotation and Preprocessing -- Chapter 3. Text Representation -- Chapter 4. Text Representation with Pretraining and Fine-tuning -- Chapter 5. Text classification -- Chapter 6. Text Clustering -- Chapter 7. Topic Model -- Chapter 8. Sentiment Analysis and Opinion Mining -- Chapter 9. Topic Detection and Tracking -- Chapter 10. Information Extraction -- Chapter 11. Automatic Text Summarization. .
520 $a This book discusses various aspects of text data mining. Unlike other books that focus on machine learning or databases, it approaches text data mining from a natural language processing (NLP) perspective. The book offers a detailed introduction to the fundamental theories and methods of text data mining, ranging from pre-processing (for both Chinese and English texts), text representation and feature selection, to text classification and text clustering. It also presents the predominant applications of text data mining, for example, topic modeling, sentiment analysis and opinion mining, topic detection and tracking, information extraction, and automatic text summarization. Bringing all the related concepts and algorithms together, it offers a comprehensive, authoritative and coherent overview. Written by three leading experts, it is valuable both as a textbook and as a reference resource for students, researchers and practitioners interested in text data mining. It can also be used for classes on text data mining or NLP.
650 0 $a Natural language processing (Computer science). $3 802180
650 0 $a Data mining. $3 528622
650 0 $a Machine learning. $3 561253
650 1 4 $a Natural Language Processing (NLP). $3 1254293
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Machine Learning. $3 1137723
700 1 $a Xia, Rui. $e author. $1 https://orcid.org/0000-0002-7376-8593 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1353421
700 1 $a Zhang, Jiajun. $e editor. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1297859
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9789811600999
776 0 8 $i Printed edition: $z 9789811601019
776 0 8 $i Printed edition: $z 9789811601026
856 4 0 $u https://doi.org/10.1007/978-981-16-0100-2
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)