000			03779nam a22004695i 4500
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007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031021718
024	7		_a10.1007/978-3-031-02171-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9.N38 _b2019 EB
100	1		_aSøgaard, Anders, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687413 _d1981-
245	1	0	_aCross-Lingual Word Embeddings _cby Anders Søgaard, Ivan Vulić, Sebastian Ruder, Manaal Faruqui
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XI, 120 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _9158738 _aProceso en lenguaje natural (Informática)
650		7	_2embne _9140153 _aSemántica
700	1		_aVulić, Ivan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687415
700	1		_aRuder, Sebastian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687414
700	1		_aFaruqui, Manaal _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687416
776	0	8	_iPrinted edition: _z9783031001826
776	0	8	_iPrinted edition: _z9783031010439
776	0	8	_iPrinted edition: _z9783031032998
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02171-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI