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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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