Machine Learning for Text (Record no. 178990)

MARC details
000 -LEADER
fixed length control field 04062nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-030-96623-2
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125531.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
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fixed length control field 220504s2022 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783030966232
-- 978-3-030-96623-2
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-030-96623-2
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5-.7
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQM
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT029000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQM
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Aggarwal, Charu C.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Machine Learning for Text
Medium [electronic resource] /
Statement of responsibility, etc by Charu C. Aggarwal.
250 ## - EDITION STATEMENT
Edition statement 2nd ed. 2022.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2022.
300 ## - PHYSICAL DESCRIPTION
Extent XXIII, 565 p. 92 illus., 5 illus. in color.
Other physical details online resource.
336 ## -
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-- txt
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-- computer
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-- online resource
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-- rdacarrier
347 ## -
-- text file
-- PDF
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505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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 ## - SUMMARY, ETC.
Summary, etc 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 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information storage and retrieval systems.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data Mining and Knowledge Discovery.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Information Storage and Retrieval.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer Nature eBook
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030966225
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030966249
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783030966256
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-3-030-96623-2">https://doi.org/10.1007/978-3-030-96623-2</a>
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

No items available.

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