[SRILM User List] Right way to build LM
stolcke at icsi.berkeley.edu
Mon Apr 28 16:20:26 PDT 2014
On 4/28/2014 3:01 AM, Ismail Rusli wrote:
> Dear all,
> I attempted to build n-gram LM from Wikipedia text. I have
> clean up all unwanted lines. I have approximately 36M words.
> I splitted the text into 90:10 proportions. Then from the 90,
> i splitted again into 4 joint training sets with increasing
> size (with the largest is about 1M sentences).
> Command i used are the followings:
> 1. Count n-gram and vocabulary:
> ngram-count -text 1M -order 3 -write count.1M -write-vocab vocab.1M -unk
> 2. Build LM with ModKN:
> ngram-count -vocab vocab.1M -read count.1M -order 3 -lm kn.lm -kndiscount
There is no need to specify -vocab if you are getting it from the same
training data as the counts.
The use of -vocab is to specify a vocabulary that differs from that of
the training data.
In fact you can combine 1 and 2 in one comment that is equivalent:
ngram-count -text 1M -order 3 -unk -lm kn.lm -kndiscount
Also, if you do use two steps, be sure to include the -unk option in the
> 3. Calculate perplexity:
> ngram -ppl test -order 3 -lm kn.lm
> My questions are:
> 1. Did i do it right?
It looks like you did.
> 2. Is there any optimization i can do in building LM?
a. Try different -order values
b. Different smoothing methods.
c. Possibly class-based models (interpolated with word-based)
d. If you want to increase training data size significantly check the
methods for conserving memory on the FAQ page.
> 3. How to calculate perplexity in log 2-based instead of log 10?
Perplexity is not dependent on the base of the logarithm (the log base
is matched by the number you exponentiate to get the ppl).
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