Low Resource Social Media Text Mining (Record no. 178105)

MARC details
000 -LEADER
fixed length control field 04201nam a22005415i 4500
001 - CONTROL NUMBER
control field 978-981-16-5625-5
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240423125443.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
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008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 211001s2021 si | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789811656255
-- 978-981-16-5625-5
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-981-16-5625-5
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q334-342
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number TA347.A78
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQ
Source thema
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Palakodety, Shriphani.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
245 10 - TITLE STATEMENT
Title Low Resource Social Media Text Mining
Medium [electronic resource] /
Statement of responsibility, etc by Shriphani Palakodety, Ashiqur R. KhudaBukhsh, Guha Jayachandran.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2021.
264 #1 -
-- Singapore :
-- Springer Nature Singapore :
-- Imprint: Springer,
-- 2021.
300 ## - PHYSICAL DESCRIPTION
Extent XI, 60 p. 14 illus., 8 illus. in color.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
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347 ## -
-- text file
-- PDF
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490 1# - SERIES STATEMENT
Series statement SpringerBriefs in Computer Science,
International Standard Serial Number 2191-5776
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Chapter 1: Introduction and outline -- Chapter 2: Natural Language Processing Preliminary -- Chapter 3: Low-Resource Multilingual Social Media Text and Challenges -- Chapter4: Robust Language Identification -- Chapter 5: Semantic Sampling -- Chapter6: Unsupervised Machine Translation.
520 ## - SUMMARY, ETC.
Summary, etc This book focuses on methods that are unsupervised or require minimal supervision—vital in the low-resource domain. Over the past few years, rapid growth in Internet access across the globe has resulted in an explosion in user-generated text content in social media platforms. This effect is significantly pronounced in linguistically diverse areas of the world like South Asia, where over 400 million people regularly access social media platforms. YouTube, Facebook, and Twitter report a monthly active user base in excess of 200 million from this region. Natural language processing (NLP) research and publicly available resources such as models and corpora prioritize Web content authored primarily by a Western user base. Such content is authored in English by a user base fluent in the language and can be processed by a broad range of off-the-shelf NLP tools. In contrast, text from linguistically diverse regions features high levels of multilinguality, code-switching, and varied languageskill levels. Resources like corpora and models are also scarce. Due to these factors, newer methods are needed to process such text. This book is designed for NLP practitioners well versed in recent advances in the field but unfamiliar with the landscape of low-resource multilingual NLP. The contents of this book introduce the various challenges associated with social media content, quantify these issues, and provide solutions and intuition. When possible, the methods discussed are evaluated on real-world social media data sets to emphasize their robustness to the noisy nature of the social media environment. On completion of the book, the reader will be well-versed with the complexity of text-mining in multilingual, low-resource environments; will be aware of a broad set of off-the-shelf tools that can be applied to various problems; and will be able to conduct sophisticated analyses of such text.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine learning.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name KhudaBukhsh, Ashiqur R.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Jayachandran, Guha.
Relator term author.
Relator code aut
-- http://id.loc.gov/vocabulary/relators/aut
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 9789811656248
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9789811656262
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title SpringerBriefs in Computer Science,
-- 2191-5776
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.1007/978-981-16-5625-5">https://doi.org/10.1007/978-981-16-5625-5</a>
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912 ## -
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942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks-CSE-Springer

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