Cross-Lingual Word Embeddings (Record no. 85015)

000 -LEADER
fixed length control field 04006nam a22005655i 4500
001 - CONTROL NUMBER
control field 978-3-031-02171-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730163827.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220601s2019 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031021718
-- 978-3-031-02171-8
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Søgaard, Anders.
245 10 - TITLE STATEMENT
Title Cross-Lingual Word Embeddings
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages XI, 120 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Human Language Technologies,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Preface -- Introduction -- Monolingual Word Embedding Models -- Cross-Lingual Word Embedding Models: Typology -- A Brief History of Cross-Lingual Word Representations -- Word-Level Alignment Models -- Sentence-Level Alignment Methods -- Document-Level Alignment Models -- From Bilingual to Multilingual Training -- Unsupervised Learning of Cross-Lingual Word Embeddings -- Applications and Evaluation -- Useful Data and Software -- General Challenges and Future Directions -- Bibliography -- Authors' Biographies.
520 ## - SUMMARY, ETC.
Summary, etc The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano--and most other languages--remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey and discuss recent and historical work on supervised and unsupervised learning of such alignments. Specifically, the book focuses on so-called cross-lingual word embeddings. The survey is intended to be systematic, using consistent notation and putting the available methods on comparable form, making it easy to compare wildly different approaches. In so doing, the authors establish previously unreported relations between these methods and are able to present a fast-growing literature in a very compact way. Furthermore, the authors discuss how best to evaluate cross-lingual word embedding methods and survey the resources available for students and researchers interested in this topic.
700 1# - AUTHOR 2
Author 2 Vulić, Ivan.
700 1# - AUTHOR 2
Author 2 Ruder, Sebastian.
700 1# - AUTHOR 2
Author 2 Faruqui, Manaal.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-02171-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2019.
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural language processing (Computer science).
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational linguistics.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Natural Language Processing (NLP).
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Linguistics.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1947-4059
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-- ZDB-2-SXSC

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