Python Untokenize a sentence

Use the join function:

You could just do a ' '.join(words) to get back the original string.

The reason tokenize.untokenize does not work is because it needs more information than just the words. Here is an example program using tokenize.untokenize:

from StringIO import StringIO
import tokenize

sentence = "I've found a medicine for my disease.n"
tokens = tokenize.generate_tokens(StringIO(sentence).readline)
print tokenize.untokenize(tokens)

Additional Help:
Tokenize – Python Docs |
Potential Problem

To reverse word_tokenize from nltk, i suggest looking in http://www.nltk.org/_modules/nltk/tokenize/punkt.html#PunktLanguageVars.word_tokenize and do some reverse engineering.

Short of doing crazy hacks on nltk, you can try this:

>>> import nltk
>>> import string
>>> nltk.word_tokenize("I've found a medicine for my disease.")
['I', "'ve", 'found', 'a', 'medicine', 'for', 'my', 'disease', '.']
>>> tokens = nltk.word_tokenize("I've found a medicine for my disease.")
>>> "".join([" "+i if not i.startswith("'") and i not in string.punctuation else i for i in tokens]).strip()
"I've found a medicine for my disease."

use token_utils.untokenize from here

import re
def untokenize(words):
    """
    Untokenizing a text undoes the tokenizing operation, restoring
    punctuation and spaces to the places that people expect them to be.
    Ideally, `untokenize(tokenize(text))` should be identical to `text`,
    except for line breaks.
    """
    text = ' '.join(words)
    step1 = text.replace("`` ", '"').replace(" ''", '"').replace('. . .',  '...')
    step2 = step1.replace(" ( ", " (").replace(" ) ", ") ")
    step3 = re.sub(r' ([.,:;?!%]+)([ '"`])', r"12", step2)
    step4 = re.sub(r' ([.,:;?!%]+)$', r"1", step3)
    step5 = step4.replace(" '", "'").replace(" n't", "n't").replace(
         "can not", "cannot")
    step6 = step5.replace(" ` ", " '")
    return step6.strip()

 tokenized = ['I', "'ve", 'found', 'a', 'medicine', 'for', 'my','disease', '.']
 untokenize(tokenized)
 "I've found a medicine for my disease."

I propose to keep offsets in tokenization: (token, offset).
I think, this information is useful for processing over the original sentence.

import re
from nltk.tokenize import word_tokenize

def offset_tokenize(text):
    tail = text
    accum = 0
    tokens = self.tokenize(text)
    info_tokens = []
    for tok in tokens:
        scaped_tok = re.escape(tok)
        m = re.search(scaped_tok, tail)
        start, end = m.span()
        # global offsets
        gs = accum + start
        ge = accum + end
        accum += end
        # keep searching in the rest
        tail = tail[end:]
        info_tokens.append((tok, (gs, ge)))
    return info_token

sent = '''I've found a medicine for my disease.

This is line:3.'''

toks_offsets = offset_tokenize(sent)

for t in toks_offsets:
(tok, offset) = t
print (tok == sent[offset[0]:offset[1]]), tok, sent[offset[0]:offset[1]]

Gives:

True I I
True 've 've
True found found
True a a
True medicine medicine
True for for
True my my
True disease disease
True . .
True This This
True is is
True line:3 line:3
True . .

I am using following code without any major library function for detokeization purpose. I am using detokenization for some specific tokens

_SPLITTER_ = r"([-.,/:!?";)(])"

def basic_detokenizer(sentence):
""" This is the basic detokenizer helps us to resolves the issues we created by  our tokenizer"""
detokenize_sentence =[]
words = sentence.split(' ')
pos = 0
while( pos < len(words)):
    if words[pos] in '-/.' and pos > 0 and pos < len(words) - 1:
        left = detokenize_sentence.pop()
        detokenize_sentence.append(left +''.join(words[pos:pos + 2]))
        pos +=1
    elif  words[pos] in '[(' and pos < len(words) - 1:
        detokenize_sentence.append(''.join(words[pos:pos + 2]))   
        pos +=1        
    elif  words[pos] in ']).,:!?;' and pos > 0:
        left  = detokenize_sentence.pop()
        detokenize_sentence.append(left + ''.join(words[pos:pos + 1]))            
    else:
        detokenize_sentence.append(words[pos])
    pos +=1
return ' '.join(detokenize_sentence)

For me, it worked when I installed python nltk 3.2.5,

pip install -U nltk

then,

import nltk
nltk.download('perluniprops')

from nltk.tokenize.moses import MosesDetokenizer

If you are using insides pandas dataframe, then

df['detoken']=df['token_column'].apply(lambda x: detokenizer.detokenize(x, return_str=True))

The reason there is no simple answer is you actually need the span locations of the original tokens in the string. If you don’t have that, and you aren’t reverse engineering your original tokenization, your reassembled string is based on guesses about the tokenization rules that were used. If your tokenizer didn’t give you spans, you can still do this if you have three things:

1) The original string

2) The original tokens

3) The modified tokens (I’m assuming you have changed the tokens in some way, because that is the only application for this I can think of if you already have #1)

Use the original token set to identify spans (wouldn’t it be nice if the tokenizer did that?) and modify the string from back to front so the spans don’t change as you go.

Here I’m using TweetTokenizer but it shouldn’t matter as long as the tokenizer you use doesn’t change the values of your tokens so that they aren’t actually in the original string.

tokenizer=nltk.tokenize.casual.TweetTokenizer()
string="One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin."
tokens=tokenizer.tokenize(string)
replacement_tokens=list(tokens)
replacement_tokens[-3]="cute"

def detokenize(string,tokens,replacement_tokens):
    spans=[]
    cursor=0
    for token in tokens:
        while not string[cursor:cursor+len(token)]==token and cursor<len(string):
            cursor+=1        
        if cursor==len(string):break
        newcursor=cursor+len(token)
        spans.append((cursor,newcursor))
        cursor=newcursor
    i=len(tokens)-1
    for start,end in spans[::-1]:
        string=string[:start]+replacement_tokens[i]+string[end:]
        i-=1
    return string

>>> detokenize(string,tokens,replacement_tokens)
'One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a cute vermin.'

from nltk.tokenize.treebank import TreebankWordDetokenizer
TreebankWordDetokenizer().detokenize(['the', 'quick', 'brown'])
# 'The quick brown'