data:image/png;base64,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

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _ 0

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _ 0

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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_, _, _, _, _, _, _o20