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dc.contributor.authorSu, Jinsongzh_CN
dc.contributor.authorZhang, Kaixuzh_CN
dc.contributor.authorDong, Huailinzh_CN
dc.contributor.author苏劲松zh_CN
dc.contributor.author张开旭zh_CN
dc.contributor.author董槐林zh_CN
dc.date.accessioned2015-07-22T07:10:35Z
dc.date.available2015-07-22T07:10:35Z
dc.date.issued2014zh_CN
dc.identifier.citationJournal of Computational Information Systems, 2014,10(4):1669-1676zh_CN
dc.identifier.issn1553-9105zh_CN
dc.identifier.other20142117743902zh_CN
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/90390
dc.description.abstractWith an important characteristic that using discriminative lexicalized reordering model to capture the phrase movement during translation, the maximum entropy based bracketing translation grammar (MEBTG) has become one of the research hotspots of statistical machine translation in recent years. However, the research of this model is far from mature. Specifically, MEBTG system tends to suffer from the problems related to over-fitting of the reordering examples. To solve this problem, we propose to apply ensemble learning framework to improve discriminative reordering model of MEBTG system. In the specific implementation, we first respectively try bagging and cross-validation to construct multiple basic classifiers, and then investigate two integration methods to combine the results of these classifiers. The experimental results on large-scale data set demonstrate the effectiveness of our method. ? 2014 Binary Information Press.zh_CN
dc.language.isoen_USzh_CN
dc.publisherBinary Information Presszh_CN
dc.source.urihttp://dx.doi.org/10.12733/jcis9471zh_CN
dc.subjectLinguisticszh_CN
dc.titleAn improved maximal entropy based bracketing transduction grammar translation model with ensemble learningzh_CN
dc.typeArticlezh_CN


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