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This held for most of the 20th century until the computer began replacing the typewriter as the primary means of creating text. In the 1990s, style guides reverted to recommending a single-space between sentences. However, instead of a slightly larger sentence space, style guides simply indicated a standard word space.
Sentence spacing concerns how spaces are inserted between sentences in typeset text and is a matter of typographical convention. [1] Since the introduction of movable-type printing in Europe, various sentence spacing conventions have been used in languages with a Latin alphabet . [ 2 ]
Unicode provides 15 variations on space width and breakability, including: THIN SPACE   and NARROW NO-BREAK SPACE  . [10] The following examples demonstrate the effect of these variations on a web browser—using space before punctuation to illustrate identical possible spacing variations following terminal punctuation.
Word spacing has the ability to express the meaning and idea behind a word, which typographers consider when working on design works and text. [9] With a written piece of text, the designer has to remember to make sure they do not add too much or too little space between words; otherwise it could ruin the texture and tone. [6]
For example, Unicode U+0020 is the "normal" space character, but U+00A0 adds the meaning that a new line should not be started there, while U+2003 represents a space with a fixed width of one em. Collectively, such characters are called Whitespace characters.
1894: the Badger-in-the-bag game—traditional typesetting spacing rules: a single enlarged em-space between sentences; 1999: the Badger-in-the-bag game—modern mass-production commercial printing: a single word space between sentences; The 1999 example demonstrates the current convention for published work.
Apple's new Messages via satellite will let you text friends and family even when you're off the grid without a cellular or Wi-Fi connection. Apple will let you send text messages via satellites ...
These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence.