class LM

[Mirjam Sepesy Maucec]

Hi all,

I have a question about the class-based models. I have just started to
use them.
First I want to understand the test example in the toolkit.
I have problems with understanding the probability computation of the
devtest.text
Can you, please, explain me, which 1grams, 2grams, 3grams.... are meant
for example in this sentence:

kaybeck and lost ok

 p( kaybeck | <s> )  = [1gram][2gram] 0.000845361 [ -3.07296 ] / 1
 p( and | kaybeck ...)  = [1gram][3gram] 0.443827 [ -0.352786 ] / 1
 p( lost | and ...)  = [2gram][2gram][4gram][4gram] 0.0305452 [ -1.51506
] / 1
 p( ok | lost ...)  = [3gram][3gram][4gram][4gram] 0.0703371 [ -1.15282
] / 0.999999
 p( </s> | ok ...)  = [3gram][4gram] 0.401395 [ -0.396428 ] / 1

I am familiar with the class model, where all words are mapped to
classes.
In this example, there are only two classes (GRIDLABEL and
SPELLED_GRIDLABEL) and
in the model we have ngrams of words and ngrams of words and classes.

I understand the idea, that if n-gram of words exists in is better to
use it
and if not, classes should help.
But what are the steps in probability computation?
Please, help!

Have a nice weekend!

Mirjam
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