國立虎尾科技大學 |

Cross-lingual word embeddings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Cross-lingual word embeddings // Anders S�gaard, Ivan Vuli�c, Sebastian Ruder, Manaal Faruq.
作者:	S�gaard, Anders,
其他作者:	Vuli�c, Ivan,
面頁冊數:	1 PDF (xi, 120 pages) : illustrations (some color).
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Natural language processing (Computer science) -
電子資源:	https://doi.org/10.2200/S00920ED2V01Y201904HLT042
電子資源:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8734027
ISBN:	9781681730646

Cross-lingual word embeddings /
S�gaard, Anders,1981-

Cross-lingual word embeddings /Anders S�gaard, Ivan Vuli�c, Sebastian Ruder, Manaal Faruq. - 1 PDF (xi, 120 pages) : illustrations (some color). - Synthesis lectures on human language technologies,#421947-4059 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 93-117).

1. Introduction -- 2. Monolingual word embedding models -- 3. Cross-lingual word embedding models : typology -- 4. A brief history of cross-lingual word representations -- 4.1. Cross-lingual word representations using bilingual lexicons -- 4.2. Cross-lingual word embeddings and word alignments -- 4.3. Representations based on latent and explicit cross-lingual concepts -- 4.4. Summary

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

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.

Mode of access: World Wide Web.

ISBN: 9781681730646

Standard No.: 10.2200/S00920ED2V01Y201904HLT042doiSubjects--Topical Terms:

641811
Natural language processing (Computer science)
Subjects--Index Terms:

natural language processing

LC Class. No.: QA76.9.N38 / S643 2019eb

Dewey Class. No.: 006.3/5

Cross-lingual word embeddings /
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245 1 0 $a Cross-lingual word embeddings / $c Anders S�gaard, Ivan Vuli�c, Sebastian Ruder, Manaal Faruq.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2019.
300 $a 1 PDF (xi, 120 pages) : illustrations (some color).
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
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490 1 $a Synthesis lectures on human language technologies, $x 1947-4059 ; $v #42
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 93-117).
505 0 $a 1. Introduction -- 2. Monolingual word embedding models -- 3. Cross-lingual word embedding models : typology -- 4. A brief history of cross-lingual word representations -- 4.1. Cross-lingual word representations using bilingual lexicons -- 4.2. Cross-lingual word embeddings and word alignments -- 4.3. Representations based on latent and explicit cross-lingual concepts -- 4.4. Summary
505 8 $a 5. Word-level alignment models -- 5.1. Word-level alignment methods with parallel data -- 5.2. Word-level alignment methods with comparable data
505 8 $a 6. Sentence-level alignment methods -- 6.1. Sentence-level methods with parallel data -- 6.2. Sentence alignment with comparable data
505 8 $a 7. Document-level alignment models -- 7.1. Document alignment with comparable data
505 8 $a 8. From bilingual to multilingual training -- 8.1. Multilingual word embeddings from word-level information -- 8.2. Multilingual word embeddings from sentence-level and document-level information
505 8 $a 9. Unsupervised learning of cross-lingual word embeddings -- 9.1. Seed dictionary induction -- 9.2. Refinement and heuristics -- 9.3. Limitations of unsupervised approaches
505 8 $a 10. Applications and evaluation -- 10.1. Intrinsic evaluation -- 10.2. Extrinsic evaluation through cross-lingual transfer -- 10.3. Multi-modal and cognitive approaches to evaluation
505 8 $a 11. Useful data and software -- 11.1. Monolingual resources -- 11.2. Cross-lingual data -- 11.3. Cross-lingual word embedding models -- 11.4. Evaluation and application -- 12. General challenges and future directions.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on June 26, 2019).
650 0 $a Natural language processing (Computer science) $3 641811
650 0 $a Machine learning. $3 561253
650 0 $a Semantic computing. $3 772728
653 $a natural language processing
653 $a machine learning
653 $a semantics
653 $a cross-lingual learning
700 1 $a Vuli�c, Ivan, $e author. $3 1253110
700 1 $a Ruder, Sebastian, $e author. $3 1253111
700 1 $a Faruq, Manaal, $e author. $3 1253112
776 0 8 $i Print version: $z 9781681730639 $z 9781681735726
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on human language technologies ; $v #18. $3 931364
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00920ED2V01Y201904HLT042
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8734027