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Pages in category "Random text generation" The following 19 pages are in this category, out of 19 total. This list may not reflect recent changes. A. AI Dungeon; B.
On Wikipedia and other sites running on MediaWiki, Special:Random can be used to access a random article in the main namespace; this feature is useful as a tool to generate a random article. Depending on your browser, it's also possible to load a random page using a keyboard shortcut (in Firefox , Edge , and Chrome Alt-Shift + X ).
A pseudoword is a unit of speech or text that appears to be an actual word in a certain language, while in fact it has no meaning.It is a specific type of nonce word, or even more narrowly a nonsense word, composed of a combination of phonemes which nevertheless conform to the language's phonotactic rules. [1]
Lexical choice is the subtask of Natural language generation that involves choosing the content words (nouns, non-auxiliary verbs, adjectives, and adverbs) in a generated text. Function words (determiners, for example) are usually chosen during realisation.
The Character Generator Protocol (CHARGEN) service is an Internet protocol intended for testing, debugging, and measurement purposes. The user receives a stream of bytes . Although the specific format of the output is not prescribed by RFC 864 , the recommended pattern (and a de facto standard ) is shifted lines of 72 ASCII characters repeating.
The Postmodernism Generator is a computer program that automatically produces "close imitations" of postmodernist writing. It was written in 1996 by Andrew C. Bulhak of Monash University using the Dada Engine, a system for generating random text from recursive grammars . [ 1 ]
Random selection, when narrowly associated with a simple random sample, is a method of selecting items (often called units) from a population where the probability of choosing a specific item is the proportion of those items in the population. For example, with a bowl containing just 10 red marbles and 90 blue marbles, a random selection ...
Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov and colleagues at Google and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words.
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