| 000 | 05418nam a2200733 i 4500 | ||
|---|---|---|---|
| 001 | 8734027 | ||
| 003 | IEEE | ||
| 005 | 20200413152932.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 190630s2019 caua fob 000 0 eng d | ||
| 020 |
_a9781681730646 _qelectronic |
||
| 020 |
_z9781681735726 _qhardcover |
||
| 020 |
_z9781681730639 _qpaperback |
||
| 024 | 7 |
_a10.2200/S00920ED2V01Y201904HLT042 _2doi |
|
| 035 | _a(CaBNVSL)thg00979210 | ||
| 035 | _a(OCoLC)1107281364 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aQA76.9.N38 _bS643 2019eb |
|
| 082 | 0 | 4 |
_a006.3/5 _223 |
| 100 | 1 |
_aSøgaard, Anders, _d1981- _eauthor. |
|
| 245 | 1 | 0 |
_aCross-lingual word embeddings / _cAnders Søgaard, Ivan Vulić, Sebastian Ruder, Manaal Faruq. |
| 264 | 1 |
_a[San Rafael, California] : _bMorgan & Claypool, _c2019. |
|
| 300 | _a1 PDF (xi, 120 pages) : illustrations (some color). | ||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aSynthesis lectures on human language technologies, _x1947-4059 ; _v#42 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 93-117). | ||
| 505 | 0 | _a1. 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 | _a5. Word-level alignment models -- 5.1. Word-level alignment methods with parallel data -- 5.2. Word-level alignment methods with comparable data | |
| 505 | 8 | _a6. Sentence-level alignment methods -- 6.1. Sentence-level methods with parallel data -- 6.2. Sentence alignment with comparable data | |
| 505 | 8 | _a7. Document-level alignment models -- 7.1. Document alignment with comparable data | |
| 505 | 8 | _a8. 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 | _a9. 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 | _a10. 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 | _a11. 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 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | ||
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 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. | ||
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on June 26, 2019). | ||
| 650 | 0 | _aNatural language processing (Computer science) | |
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aSemantic computing. | |
| 653 | _anatural language processing | ||
| 653 | _amachine learning | ||
| 653 | _asemantics | ||
| 653 | _across-lingual learning | ||
| 700 | 1 |
_aVulić, Ivan, _eauthor. |
|
| 700 | 1 |
_aRuder, Sebastian, _eauthor. |
|
| 700 | 1 |
_aFaruq, Manaal, _eauthor. |
|
| 776 | 0 | 8 |
_iPrint version: _z9781681730639 _z9781681735726 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on human language technologies ; _v#42. |
|
| 856 | 4 | 0 |
_3Abstract with links to full text _uhttps://doi.org/10.2200/S00920ED2V01Y201904HLT042 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/servlet/opac?bknumber=8734027 |
| 999 |
_c562418 _d562418 |
||