Hongqin,
there is no guarantee that a class-based LM will have lower perplexity
than a word-based one. For small, task-oriented domains with little
training data (think ATIS), you can usually get a good improvement
with hand-defined word classes that reflect the properties of
the domain. For large-vocabulary, unconstrainted domains (such as
Switchboard or Broadcast News), a class-based LM by itself will usually
have higher perplexity. However, you can usually get a nice
perplexity reduction by interpolating the word and the class-based LMs.
Mostly, the class-based LM helps with the prediction of unseen word ngrams.
It is pure laziness that the make-ngram-pfsg script cannot handle
4-gram and higher-order LMs at this point. It shouldn't be hard to
do. If anybody wants to contribute a generalized version I'd be happy
to incorporate it.
--Andreas
In message <3C911CC9.C47D16B1 at ADDRESS HIDDEN>you wrote:
> Hi,
>
> I am trying to construct a class based trigram LM. The function
> "ngram-class" only induces classes for a bigram model. I have my own
> class definitions with the class-format. When I use these definition
> with the "ngram" function (-classes option), the LM leads to a higher
> perplexity and word error rate than those from a word based trigram. Is
> there any other approach with which I can get a class-based LM with
> lower perplexity the a word-based?
>
> By the way, anyone tried a 4gram model with pfsg format?
>
> Thanks!
>
> Hongqin Liu
>
>
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