000 04006nam a22005655i 4500
001 978-3-031-02171-8
003 DE-He213
005 20240730163827.0
007 cr nn 008mamaa
008 220601s2019 sz | s |||| 0|eng d
020 _a9783031021718
_9978-3-031-02171-8
024 7 _a10.1007/978-3-031-02171-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSøgaard, Anders.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980690
245 1 0 _aCross-Lingual Word Embeddings
_h[electronic resource] /
_cby Anders Søgaard, Ivan Vulić, Sebastian Ruder, Manaal Faruqui.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXI, 120 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
505 0 _aPreface -- 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 _aThe 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aComputational linguistics.
_96146
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputational Linguistics.
_96146
700 1 _aVulić, Ivan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980691
700 1 _aRuder, Sebastian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980692
700 1 _aFaruqui, Manaal.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980693
710 2 _aSpringerLink (Online service)
_980694
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001826
776 0 8 _iPrinted edition:
_z9783031010439
776 0 8 _iPrinted edition:
_z9783031032998
830 0 _aSynthesis Lectures on Human Language Technologies,
_x1947-4059
_980695
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02171-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85015
_d85015