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 es:todos menos la primera letra de _ pl:_ bez pierwszej litery

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 es:todas menos la primera palabra de _ pl:_ bez pierwszego słowa

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 es:todos menos la última letra de _ pl:_ bez ostatniej litery

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 es:todos menos la última palabra de _ pl:_ bez ostatniego słowa

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _ es:primera palabra de _ pl:pierwsze słowo _

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

pt:o último caractere de _ ca:primera lletra de _ es:última letra de _ pl:ostatnia litera _ 0

pt:a última palavra de _ ca:darrera paraula de _ es:última palabra de _ pl:ostatnie słowo _

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? _ es:¿palabra vacía? _ pl:puste słowo? _ 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? _ es:¿oración vacía? _ pl:puste zdanie? _

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 _ pl:połącz słowa _ 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 _ pl:lista $arrowRight zdanie _

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 _ pl:zdanie $arrowRight lista _

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 _ pl:słowo $arrowRight lista _

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 _ pl:lista $arrowRight słowo _

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 _ es:oración _ pl:zdanie _

pt:lança o erro _ pl:błąd _

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

ca:notació textual de _ es:imprimible _ pl:tekstowa _ (

)

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.

es:requerir no vacío _ _

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

pt:a primeira palavra de _ ca:primera lletra de _ es:primera letra de _ pl:pierwsza litera _

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