Novelang already does all the typesetting for you. What’s next? Writing text, of course! The just-started Novelist subproject, which aims to generate big documents for Novelang testing under heavy load.
Based on French metrics, random text looks like this:
Uomuecto eaufues xuner ig ocanerr, ebanu otpaa. Uuse, on eian aibtd, rttaintlufe elvettarrh, yrn enemlcmlun, ebcazepuer madscg, êiiovemtt teeost eseeerde? Fetn eearréetcs emrseoss icia ntmvesrud. Aoasro cênit ctainetda aèugedet css eali, unero aaie eneoden, nrortio. Oovlod; tfsmenco méttsna, eesdis uoeaeanao rcuent, desungtt av au oneerao, dxuaste umeinétniu lccdeiilne rliùearde veyiritisac yàslu. Iinmseuo odiapqied cmiiapearlo ebnjtus uauueis, libginmasa edrc emaèi sllieyr sode!
It bases on simplistic distribution algorithm. Word count and letter count from uniform distribution in a pre-defined range (something like 5-20 for words and 2-12 for letters). Letters come from a frequency table giving the percentage of appearance for each letter.
While the result doesn’t look much like real text, it’s good enough to stress basic parsing and typesetting.
There has been a lot of research about text analysis, first for cryptography, next for natural language analysis and Web crawling. Among all of them, there is a nifty one: the n-grams , which describe all the different letter sequences of a fixed length in a given text. The demo on Wolfram Alpha is gorgeous. It shows how combinations grow fast: a simple sentence like “ceramics come from” contains 69 3-grams. Google’s n-grams database (ranging from 1-grams to 5-grams) weights 24 GiB gzip’ed and contains near 1 billion of 3-grams. Amazingly, this number doesn’t increase so much for 4-grams and 5-grams.