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 _

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? _

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 _

false

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