* `RESTRICT_URL`: truncate urls till non-whitespace ASCII ( in the ASCII table)įor Chinese users, we recommend using `RESTRICT_URL`.įrom text_cleaner.processor. * `CHINESE`: common characters symbols and puntuations. * `CHINESE_CHARACTER`: only common characters. Read the source code if you are sure about what's going on. * *ranges*: iterable of instances of *UnicodeRange*.įollowing processors are defined by *UnicodeRange* and regex. *UnicodeRangeProcessor(ranges, replace\_text=DEFAULT\_REPLACE\_TEXT)* * *end*: *int*, the end of unicode range. * *begin*: *int*, the begin of unicode range. * *verify(self, text)*: return *True* if text match *regex*, otherwise returns *False*. text-cleaner requires: Python (>3.8) BeautifulSoup (4.9.3. Dependencies Installation Instructions License Dependencies. This tool helps to remove noise from text and make it ready to feed to models. This site doesn't save or store any data you enter. text-cleaner is a tool created to perform NLP Text preprocessing. Remove email indents, find and replace, clean up spacing, line breaks, word characters and more. * *keep(self, text)*: keep only the occurences of *regex*, remove all unmatched components from *text*. The quick, easy, web based way to fix and clean up text when copying and pasting between applications. * *remove(self, text)*: remove all occurences of *regex* from *text*. * *replace(self, new\_replace\_text)*: create a new processor, with new *replace\_text* is set. * contruct a regex processor for *regex*, replace unmatched components with *replace\_text*. ![]() *RegexProcessor(regex, replace\_text=DEFAULT\_REPLACE\_TEXT)* *DEFAULT\_REPLACE\_TEXT*: `' '`, single space. * same as *remove*, but invoke `keep` method of processors instead. What I am thinking is having some kind of list containing words to keep, and incorporating this into my function to avoid. ![]() *remove* invokes `remove` of each processor to handle *text*. Here is the code I have so far: def cleannoneng (text): words set ( ()) text ' '.join (w for w in nltk.wordpuncttokenize (text) if w.lower () in words or not w.isalpha ()) return text. 1) Clear out HTML characters: A Lot of HTML entities like ' ,
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