给定源语言句子，系统在所有候选目标语言句子中，基于统计模型选择概率最大的句子作为翻译结果。
Given a source sentence, and based on the statistical model, the system selects the string with the highest probability by statistical model from all possible target sentences.
本文研究的目的是建立基于词上下文的汉语统计语言模型。
The aim of this thesis is to construct a word-based context Chinese language model.
统计机器翻译是利用基于语料库训练得到的统计参数模型，将源语言的文本翻译成目标语言，它是机器翻译的主流方向。
Statistical machine translation (SMT) is the text translation by the statistical parameter models obtained from the training corpus, which has become the mainstream of machine translation research.
在本文中，我们提出了一种统一的统计语言模型方法用来汉语自动分词和中文命名实体识别，这种方法对基于词的三元语言模型进行了很好的扩展。
In this paper, we extend a word-based trigram modeling to Chinese word segmentation and Chinese named entity recognition, by proposing a unified approach to SLM.
在本文中，我们提出了一种统一的统计语言模型方法用来汉语自动分词和中文命名实体识别，这种方法对基于词的三元语言模型进行了很好的扩展。
In this paper, we extend a word-based trigram modeling to Chinese word segmentation and Chinese named entity recognition, by proposing a unified approach to SLM.
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