我的英文句子。现在我想混杂的话并输入该组词成一个计划，要根据英语语法的正常规则来输出原句解读的话。我隐约相信它会需要自然语言生成算法。

I have a sentence in English. Now I want to jumble the words up and input that set of words into a program which should unscramble the words according to normal rules of English grammar to output the original sentence. I can vaguely assume it would require Natural Language Generation algorithms.

有关，例如：

输出应该是同一句话。

The output should be the same sentence.

我只能假设为一组的话永远不够，产生了原判。但是，更多的信息必须包括恢复原句？ 请指引我到哪里我应该开始。

I can assume only the set of words will never be enough to generate the original sentence. But what more information must be included to revive the original sentence? Please guide me as to where I should be starting with.

推荐答案

语言模型事情基于如何做好典范承认的文本可以在文本或句子（字数任何序列），并为其分配一个概率。

Language models are things that can take in a text or sentence (any sequence of words) and assign it a probability based on how well the model "recognizes" that text.

要解决你的问题，你可以采取一个语言模型，并用它来计算每个可能的排列可以使输入字的概率。最有可能的句子协议的模式可能是最一致的。

To solve your problem, you could take a language model and use it to compute the probability of each possible permutation you can make of the input words. The most probable sentence accord to the model is probably the most coherent one.

有关的情况像你这样，尝试了N元模型（对n> 2 ..我想2个或3应该做的伎俩）或隐马尔可夫模型，利用语音标签应该做的伎俩的一部分。

For a situation like yours, trying a n-gram model (for n > 2.. I think 2 or 3 should do the trick) or a Hidden Markov model leveraging part of speech tags should do the trick.