國立虎尾科技大學 |

Fundamentals of Predictive Text Mining

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Fundamentals of Predictive Text Mining/ by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang.
Author:	Weiss, Sholom M.
other author:	Indurkhya, Nitin.
Description:	XIII, 239 p. 115 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Data mining. -
Online resource:	https://doi.org/10.1007/978-1-4471-6750-1
ISBN:	9781447167501

Fundamentals of Predictive Text Mining
Weiss, Sholom M.

Fundamentals of Predictive Text Mining[electronic resource] /by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang. - 2nd ed. 2015. - XIII, 239 p. 115 illus.online resource. - Texts in Computer Science,1868-0941. - Texts in Computer Science,.

Overview of Text Mining -- From Textual Information to Numerical Vectors -- Using Text for Prediction -- Information Retrieval and Text Mining -- Finding Structure in a Document Collection -- Looking for Information in Documents -- Data Sources for Prediction: Databases, Hybrid Data and the Web -- Case Studies -- Emerging Directions.

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

ISBN: 9781447167501

Standard No.: 10.1007/978-1-4471-6750-1doiSubjects--Topical Terms:

528622
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Fundamentals of Predictive Text Mining
LDR:03508nam a22004215i 4500 001 960393
003 DE-He213
005 20200704160849.0
007 cr nn 008mamaa
008 201211s2015 xxk| s |||| 0|eng d
020 $a 9781447167501 $9 978-1-4471-6750-1
024 7 $a 10.1007/978-1-4471-6750-1 $2 doi
035 $a 978-1-4471-6750-1
050 4 $a QA76.9.D343
072 7 $a UNF $2 bicssc
072 7 $a COM021030 $2 bisacsh
072 7 $a UNF $2 thema
072 7 $a UYQE $2 thema
082 0 4 $a 006.312 $2 23
100 1 $a Weiss, Sholom M. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 792410
245 1 0 $a Fundamentals of Predictive Text Mining $h [electronic resource] / $c by Sholom M. Weiss, Nitin Indurkhya, Tong Zhang.
250 $a 2nd ed. 2015.
264 1 $a London : $b Springer London : $b Imprint: Springer, $c 2015.
300 $a XIII, 239 p. 115 illus. $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
490 1 $a Texts in Computer Science, $x 1868-0941
505 0 $a Overview of Text Mining -- From Textual Information to Numerical Vectors -- Using Text for Prediction -- Information Retrieval and Text Mining -- Finding Structure in a Document Collection -- Looking for Information in Documents -- Data Sources for Prediction: Databases, Hybrid Data and the Web -- Case Studies -- Emerging Directions.
520 $a This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies. This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, and errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation. Topics and features: Presents a comprehensive, practical and easy-to-read introduction to text mining Includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter Explores the application and utility of each method, as well as the optimum techniques for specific scenarios Provides several descriptive case studies that take readers from problem description to systems deployment in the real world Describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English) Contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.
650 0 $a Data mining. $3 528622
650 0 $a Natural language processing (Computer science). $3 802180
650 0 $a Application software. $3 528147
650 0 $a Information storage and retrieval. $3 1069252
650 0 $a Database management. $3 557799
650 1 4 $a Data Mining and Knowledge Discovery. $3 677765
650 2 4 $a Natural Language Processing (NLP). $3 1254293
650 2 4 $a Computer Appl. in Administrative Data Processing. $3 669633
650 2 4 $a Information Storage and Retrieval. $3 593926
650 2 4 $a Database Management. $3 669820
700 1 $a Indurkhya, Nitin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 839646
700 1 $a Zhang, Tong. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1069124
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781447167495
776 0 8 $i Printed edition: $z 9781447167518
776 0 8 $i Printed edition: $z 9781447171133
830 0 $a Texts in Computer Science, $x 1868-0941 $3 1254292
856 4 0 $u https://doi.org/10.1007/978-1-4471-6750-1
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)