Fojiba-Jabba uses techniques from Markov Chain- and Recursive Transition Network-Theory.
One method of text generation involves Markov Chains. In theory, Markov Chains can produce a delightfully quirky text; in practice, they sort of suck.
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.
There are, however, several problems with this method:
- The corpus available is too limited to attempt anything but an Order-1 Markov Chain (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.
- 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
Another method of text generation involves Recursive Transition Networks. While more grammatical than Markov Chains by design, they are slightly more difficult to implement and, unless cleverly manufactured, have less of the idiosyncratic charm that Markov Chain-fans find so endearing.