公开(公告)号：US20090192781A1

公开(公告)日：2009-07-30

申请号：US12022819

申请日：2008-01-30

申请人： Srinivas BANGALORE ,  Emil Ettelaie

发明人： Srinivas BANGALORE ,  Emil Ettelaie

IPC分类号： G06F17/28

CPC分类号： G06F17/2827

摘要： A machine translation method, system for using the method, and computer readable media are disclosed. The method includes the steps of receiving a source language sentence, selecting a set of target language n-grams using a lexical classifier and based on the source language sentence. When selecting the set of target language n-grams, in at least one n-gram, n is greater than 1. The method continues by combining the selected set of target language n-grams as a finite state acceptor (FSA), weighting the FSA with data from the lexical classifier, and generating an n-best list of target sentences from the FSA. As an alternate to using the FSA, N strings may be generated from the n-grams and ranked using a language model. The N strings may be represented by an FSA for efficiency but it is not necessary.

摘要翻译： 公开了一种机器翻译方法，使用该方法的系统和计算机可读介质。 该方法包括以下步骤：接收源语言句，使用词法分类器并基于源语言句选择一组目标语言n-gram。 当选择一组目标语言n-gram时，在至少一个n-gram中，n大于1.该方法通过将所选择的一组目标语言n-gram组合为有限状态接收器（FSA）来继续加权， FSA与来自词汇分类器的数据，并从FSA生成目标句子的最佳列表。 作为使用FSA的替代方案，可以使用n-gram生成N个字符串，并使用语言模型进行排序。 N字符串可以由FSA表示以提高效率，但不是必需的。

公开(公告)号：US08849665B2

公开(公告)日：2014-09-30

申请号：US12022819

申请日：2008-01-30

申请人： Srinivas Bangalore ,  Emil Ettelaie

发明人： Srinivas Bangalore ,  Emil Ettelaie

IPC分类号： G10L15/00 ,  G10L15/18 ,  G06F17/28

CPC分类号： G06F17/2827

摘要： A machine translation method, system for using the method, and computer readable media are disclosed. The method includes the steps of receiving a source language sentence, selecting a set of target language n-grams using a lexical classifier and based on the source language sentence. When selecting the set of target language n-grams, in at least one n-gram, n is greater than 1. The method continues by combining the selected set of target language n-grams as a finite state acceptor (FSA), weighting the FSA with data from the lexical classifier, and generating an n-best list of target sentences from the FSA. As an alternate to using the FSA, N strings may be generated from the n-grams and ranked using a language model. The N strings may be represented by an FSA for efficiency but it is not necessary.

摘要翻译： 公开了一种机器翻译方法，使用该方法的系统和计算机可读介质。 该方法包括以下步骤：接收源语言句，使用词法分类器并基于源语言句选择一组目标语言n-gram。 当选择一组目标语言n-gram时，在至少一个n-gram中，n大于1.该方法通过将所选择的一组目标语言n-gram组合为有限状态接收器（FSA）来继续加权， FSA与来自词汇分类器的数据，并从FSA生成目标句子的最佳列表。 作为使用FSA的替代方案，可以使用n-gram生成N个字符串，并使用语言模型进行排序。 N字符串可以由FSA表示以提高效率，但不是必需的。