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Ambigrams can be constructed in various languages and alphabets, and the notion often extends to numbers and other symbols. It is a recent interdisciplinary concept, combining art, literature, mathematics, cognition, and optical illusions. Drawing symmetrical words constitutes also a recreational activity for amateurs.
Interlinear gloss. In linguistics and pedagogy, an interlinear gloss is a gloss (series of brief explanations, such as definitions or pronunciations) placed between lines, such as between a line of original text and its translation into another language. When glossed, each line of the original text acquires one or more corresponding lines of ...
In linguistics, a blend—also known as a blend word, lexical blend, or portmanteau [a] —is a word formed, usually intentionally, by combining the sounds and meanings of two or more words. [ 2 ] [ 3 ] [ 4 ] English examples include smog , coined by blending smoke and fog , [ 3 ] [ 5 ] as well as motel , from motor ( motorist ) and hotel . [ 6 ]
Letter Boxed. Letter Boxed is an online word puzzle video game created by Sam Ezersky and published in 2019 (soft-launched in 2018) on The New York Times Games. [1] It was the third game published in the puzzles section on the New York Times website after the Crossword and Spelling Bee. [2] Originally created as part of an effort to attract new ...
Linguistic typology. In linguistic typology , object–subject (OS) word order, also called O-before-S or patient–agent word order , is a word order in which the object appears before the subject. OS is notable for its statistical rarity as a default or predominant word order among natural languages. [1] Languages with predominant OS word ...
A table is an arrangement of columns and rows that organizes and positions data or images. Tables can be created on Wikipedia pages using special wikitext syntax, and many different styles and tricks can be used to customise them.
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Lexical tokenization is related to the type of tokenization used in large language models (LLMs) but with two differences. First, lexical tokenization is usually based on a lexical grammar, whereas LLM tokenizers are usually probability -based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values.