NLTK, Random Sentence Generators?

[Passer By]

Has any created or not of examples of random sentence generators using
n-gram models (or other models might be interesting).

I know of one example from a course at MIT, but besides that nothing.

Any help would be great.

 

[James Stroud]

Has any created or not of examples of random sentence generators using
n-gram models (or other models might be interesting).

I know of one example from a course at MIT, but besides that nothing.

Any help would be great.

Best is to just cull text from your spam folder as these are often
generated by similar means--but somehow I think you knew that.

James

 

[Passer By]

Best is to just cull text from your spam folder as these are often
generated by similar means--but somehow I think you knew that.

Emotive on Usenet? Thats original.

 

[gene tani]

Has any created or not of examples of random sentence generators using
n-gram models (or other models might be interesting).

I know of one example from a course at MIT, but besides that nothing.

Any help would be great.

Markov chains e.g.
http://rubyquiz.com/quiz74.html

 

[Paulo da Silva]

gene tani escreveu:

Markov chains e.g.
http://rubyquiz.com/quiz74.html

Seems interesting ... Is there anybody who masters both python and ruby
to "translate" the code into python?

 

[mensanator]

gene tani escreveu:



Seems interesting ... Is there anybody who masters both python and ruby
to "translate" the code into python?

Wouldn't it be easier to translate Python:

<http://www.strout.net/python/shaney.py>

 

[James Stroud]

Emotive on Usenet? Thats original.

Anonimity on usenet? That's suspiscious.

 

[Passer By]

Anonimity on usenet? That's suspiscious.

Paranoid Plunk?

 

[Oleg Alexander]

Paranoid Plunk?- Hide quoted text -

- Show quoted text -

Another way to generate random text is by using context free grammars
and recursive substitution. Here are some examples of programs which
use this technique:

http://nonsense.sourceforge.net/
http://www.elsewhere.org/pomo

The programs generate from a user-defined "grammar" file, which looks
something like this:

ROOT
$sentence

SENTENCE
I $feelabout my $animal!

FEELABOUT
love
hate

ANIMAL
dog
cat

The above grammar will generate the following possible sentences:

I love my cat!
I hate my cat!
I love my dog!
I hate my dog!

Unlike a Markov generator, the output of these programs is
grammatically correct.