| 000 | 03478nam a2200493 i 4500 | ||
|---|---|---|---|
| 001 | 6267198 | ||
| 003 | IEEE | ||
| 005 | 20220712204556.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151223s2008 maua ob 001 eng d | ||
| 020 |
_a9780262255097 _qebook |
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| 020 |
_z026225509X _qelelelectronic |
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| 020 |
_z9780262072977 _qprint |
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| 035 | _a(CaBNVSL)mat06267198 | ||
| 035 | _a(IDAMS)0b000064818b4160 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aP309 _b.L43 2009eb |
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| 082 | 0 | 4 |
_a418/.020285 _222 |
| 245 | 0 | 0 |
_aLearning machine translation / _c[edited by] Cyril Goutte ... [et al.]. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc2009. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2008] |
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| 300 |
_a1 PDF (xii, 316 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aNeural information processing series | |
| 504 | _aIncludes bibliographical references (p. [277]-306) and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aThe Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a thriving research field with enormous commercial potential. This volume investigates how Machine Learning techniques can improve Statistical Machine Translation, currently at the forefront of research in the field. The book looks first at enabling technologies--technologies that solve problems that are not Machine Translation proper but are linked closely to the development of a Machine Translation system. These include the acquisition of bilingual sentence-aligned data from comparable corpora, automatic construction of multilingual name dictionaries, and word alignment. The book then presents new or improved statistical Machine Translation techniques, including a discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination of multiple Machine Translation outputs in order to improve overall translation quality.ContributorsSrinivas Bangalore, Nicola Cancedda, Josep M. Crego, Marc Dymetman, Jakob Elming, George Foster, Jesƒus Gim�nez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mah�, Llu�s M�rquez, Evgeny Matusov, I. Dan Melamed, Ion Muslea, Hermann Ney, Bruno Pouliquen, Dan Roth, Anoop Sarkar, John Shawe-Taylor, Ralf Steinberger, Joseph Turian, Nicola Ueffing, Masao Utiyama, Zhuoran Wang, Benjamin Wellington, Kenji Yamada. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 550 | _aMade available online by Ebrary. | ||
| 588 | _aDescription based on PDF viewed 12/23/2015. | ||
| 650 | 0 |
_aMachine translating _xStatistical methods. _921447 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aGoutte, Cyril. _921448 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _921449 |
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| 710 | 2 |
_aMIT Press, _epublisher. _921450 |
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| 776 | 0 | 8 |
_iPrint version _z9780262072977 |
| 830 | 0 |
_aNeural information processing series _921451 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267198 |
| 942 | _cEBK | ||
| 999 |
_c72856 _d72856 |
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