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Goutte, Cyril (EDT)/ Cancedda, Nicola (EDT)/ Dymetman, Marc (EDT)/ Foster, George (EDT) 作者

The MIT Press

译者

2009-02-27 出版日期

328 页数

USD 45.00 价格

Hardcover

丛书系列

9780262072977 图书编码

Learning Machine Translation (Neural Information Processing) 在线电子书图书标签: 机器翻译计算语言学计算机科学科学 Translation Machine MIT Learning

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Learning Machine Translation (Neural Information Processing) 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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The 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, Jesus Gimenez, Cyril Goutte, Nizar Habash, Gholamreza Haffari, Patrick Haffner, Hitoshi Isahara, Stephan Kanthak, Alexandre Klementiev, Gregor Leusch, Pierre Mahe, Lluis Marquez, 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

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