Difference between revisions of "Fojiba-Jabba Notes"

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[[Fojiba-Jabba]] is an anthropomorphic Artificial Intelligence application, ostensibly the module of [[Cruft Alarm]] that supports Natural Language Generation.
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[[Fojiba-Jabba]] is the module of [[Cruft Alarm]] supporting Automatic Text Generation.
  
 
=Theoretical Foundations=
 
=Theoretical Foundations=
Fojiba-Jabba utilizes a hybrid of Markov Chain- and Recursive Transition Network-Theory in order to mimic Natural Language.
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Fojiba-Jabba uses techniques from Markov Chain- and Recursive Transition Network-Theory.
  
 
==Markov Chains==
 
==Markov Chains==
Often ungrammatical.
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One method of text generation involves Markov Chains. In theory, Markov Chains can produce a delightfully quirky text; in practice, they sort of suck.
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===Process===
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The process can be summarized as follows:
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*The user specifies an initial word and the number of sentences desired in the text.
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*Fojiba-Jabba, having previously analyzed a set of texts in order to gather statistics on which words follow which words, uses these data to generate the next word.
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*This process repeats until the desired number of sentences is obtained.
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===Problems===
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There are, however, several problems with this method:
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*The corpus available is too limited to attempt anything but an Order-1 Markov Chain, as anything higher results in what is essentially the original text itself.
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*An Order-1 Markov Chain is often too retarded to produce anything but rather ungrammatical (and clearly fake) sentences.
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===Possible Solutions===
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*Use highly advanced linguistic knowledge to improve grammaticality (e.g., a noun or an adjective must follow a determiner). A Brill Part-of-Speech Tagger or the Stanford Parser may be useful here.
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*Use google to find likely following words, or to increase the dataset somehow.
  
 
==Recursive Transition Networks==
 
==Recursive Transition Networks==
 
Less idiosyncratic.
 
Less idiosyncratic.

Revision as of 20:01, 8 June 2006

Fojiba-Jabba is the module of Cruft Alarm supporting Automatic Text Generation.

Theoretical Foundations

Fojiba-Jabba uses techniques from Markov Chain- and Recursive Transition Network-Theory.

Markov Chains

One method of text generation involves Markov Chains. In theory, Markov Chains can produce a delightfully quirky text; in practice, they sort of suck.

Process

The process can be summarized as follows:

  • The user specifies an initial word and the number of sentences desired in the text.
  • Fojiba-Jabba, having previously analyzed a set of texts in order to gather statistics on which words follow which words, uses these data to generate the next word.
  • This process repeats until the desired number of sentences is obtained.

Problems

There are, however, several problems with this method:

  • The corpus available is too limited to attempt anything but an Order-1 Markov Chain, as anything higher results in what is essentially the original text itself.
  • An Order-1 Markov Chain is often too retarded to produce anything but rather ungrammatical (and clearly fake) sentences.

Possible Solutions

  • Use highly advanced linguistic knowledge to improve grammaticality (e.g., a noun or an adjective must follow a determiner). A Brill Part-of-Speech Tagger or the Stanford Parser may be useful here.
  • Use google to find likely following words, or to increase the dataset somehow.

Recursive Transition Networks

Less idiosyncratic.