先看一下语言模型的输出格式
\datangram 1=64000
ngram 2=522530
ngram 3=173445
\1-grams:
-5.24036 ‘cause -0.2084827
-4.675221 ‘em -0.221857
-4.989297 ‘n -0.05809768
-5.365303 ‘til -0.1855581
-2.111539 </s> 0.0
-99 <s> -0.7736475
-1.128404 <unk> -0.8049794
-2.271447 a -0.6163939
-5.174762 a‘s -0.03869072
-3.384722 a. -0.1877073
-5.789208 a.‘s 0.0
-6.000091 aachen 0.0
-4.707208 aaron -0.2046838
-5.580914 aaron‘s -0.06230035
-5.789208 aarons -0.07077657
-5.881973 aaronson -0.2173971
（注：上面的值都是以10为底的对数值）
上面是一个语言模型的一部分，三元语言模型的综合格式如下：
\data
ngram 1=nr # 一元语言模型
ngram 2=nr # 二元语言模型
ngram 3=nr # 三元语言模型
\1-grams:
pro_1 word1 back_pro1
\2-grams:
pro_2 word1 word2 back_pro2
\3-grams:
pro_3 word1 word2 word3
\end\
第一项表示ngram的条件概率，就是P(wordN | word1，word2，。。。，wordN-1）。
第二项表示ngram的词。
最后一项是回退的权重。
举例来说，对于三个连续的词来说，我们计算三个词一起出现的概率：
P(word3|word1,word2)
表示word1和word2出现的情况下word3出现的概率，比如P(平|习，进）的意思是已经出现了“习进”两个字，后面是平的概率，这个概率这么计算：
if(存在(word1,word2,word3)的三元模型){
return pro_3(word1,word2,word3) ;
}else if(存在(word1,word2)二元模型){
return back_pro2(word1,word2)*P(word3|word2) ;
}else{
return P(word3 | word2);
}
上面的计算又集中在计算P(word3 | word2)的概率上，就是如果不存在习进平的三元模型，此时不管何种路径，都要计算P(word3 | word2) 的概率，计算如下：
if(存在(word1,word2)的二元模型){
return pro_2(word1,word2);
}else{
return back_pro2(word1)*pro_1(word2) ;
}
