I am using python to clean a given sentence. Suppose that my sentence is:
What's the best way to ensure this?
I want to convert:
What's -> What is
Similarly,
must've -> must have
Also, verbs to original form,
told -> tell
Singular to plural, and so on.
I am currently exploring textblob. But not all of the above is possible using it.
For the first question, there isn't a direct module that does that for you so you will have to build your own, first you will need a contraction dictionary like this one:
contractions = {
"ain't": "am not / are not",
"aren't": "are not / am not",
"can't": "cannot",
"can't've": "cannot have",
"'cause": "because",
"could've": "could have",
"couldn't": "could not",
"couldn't've": "could not have",
"didn't": "did not",
"doesn't": "does not",
"don't": "do not",
"hadn't": "had not",
"hadn't've": "had not have",
"hasn't": "has not",
"haven't": "have not",
"he'd": "he had / he would",
"he'd've": "he would have",
"he'll": "he shall / he will",
"he'll've": "he shall have / he will have",
"he's": "he has / he is",
"how'd": "how did",
"how'd'y": "how do you",
"how'll": "how will",
"how's": "how has / how is",
"i'd": "I had / I would",
"i'd've": "I would have",
"i'll": "I shall / I will",
"i'll've": "I shall have / I will have",
"i'm": "I am",
"i've": "I have",
"isn't": "is not",
"it'd": "it had / it would",
"it'd've": "it would have",
"it'll": "it shall / it will",
"it'll've": "it shall have / it will have",
"it's": "it has / it is",
"let's": "let us",
"ma'am": "madam",
"mayn't": "may not",
"might've": "might have",
"mightn't": "might not",
"mightn't've": "might not have",
"must've": "must have",
"mustn't": "must not",
"mustn't've": "must not have",
"needn't": "need not",
"needn't've": "need not have",
"o'clock": "of the clock",
"oughtn't": "ought not",
"oughtn't've": "ought not have",
"shan't": "shall not",
"sha'n't": "shall not",
"shan't've": "shall not have",
"she'd": "she had / she would",
"she'd've": "she would have",
"she'll": "she shall / she will",
"she'll've": "she shall have / she will have",
"she's": "she has / she is",
"should've": "should have",
"shouldn't": "should not",
"shouldn't've": "should not have",
"so've": "so have",
"so's": "so as / so is",
"that'd": "that would / that had",
"that'd've": "that would have",
"that's": "that has / that is",
"there'd": "there had / there would",
"there'd've": "there would have",
"there's": "there has / there is",
"they'd": "they had / they would",
"they'd've": "they would have",
"they'll": "they shall / they will",
"they'll've": "they shall have / they will have",
"they're": "they are",
"they've": "they have",
"to've": "to have",
"wasn't": "was not",
"we'd": "we had / we would",
"we'd've": "we would have",
"we'll": "we will",
"we'll've": "we will have",
"we're": "we are",
"we've": "we have",
"weren't": "were not",
"what'll": "what shall / what will",
"what'll've": "what shall have / what will have",
"what're": "what are",
"what's": "what has / what is",
"what've": "what have",
"when's": "when has / when is",
"when've": "when have",
"where'd": "where did",
"where's": "where has / where is",
"where've": "where have",
"who'll": "who shall / who will",
"who'll've": "who shall have / who will have",
"who's": "who has / who is",
"who've": "who have",
"why's": "why has / why is",
"why've": "why have",
"will've": "will have",
"won't": "will not",
"won't've": "will not have",
"would've": "would have",
"wouldn't": "would not",
"wouldn't've": "would not have",
"y'all": "you all",
"y'all'd": "you all would",
"y'all'd've": "you all would have",
"y'all're": "you all are",
"y'all've": "you all have",
"you'd": "you had / you would",
"you'd've": "you would have",
"you'll": "you shall / you will",
"you'll've": "you shall have / you will have",
"you're": "you are",
"you've": "you have"
}
Then write some code to modify your text according to the dictionary, something like this:
text="What's the best way to ensure this?"
for word in text.split():
if word.lower() in contractions:
text = text.replace(word, contractions[word.lower()])
print(text)
For your second question on changing verb tense, nodebox's linguistics library is very popular and highly recommended for such tasks. After downloading their zip file, unzip it and copy it to python's site-package directory. After doing that, you can write something like this:
import en
for word in text.split():
if en.is_verb(word.lower()):
text = text.replace(word, en.verb.present(word.lower()))
print text
Note: this library is only for Python 2 since it does not yet offer support for python 3.
The answers above will work perfectly well and could be better for ambiguous contraction (although I would argue that there aren't that much of ambiguous cases). I would use something that is more readable and easier to maintain:
import re
def decontracted(phrase):
# specific
phrase = re.sub(r"won\'t", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
test = "Hey I'm Yann, how're you and how's it going ? That's interesting: I'd love to hear more about it."
print(decontracted(test))
# Hey I am Yann, how are you and how is it going ? That is interesting: I would love to hear more about it.
It might have some flaws I didn't think about though.
If you want to roll your own, you can use this for contraction mapping:
http://alicebot.blogspot.com/2009/03/english-contractions-and-expansions.html
And this for verb replacements:
http://www.lexically.net/downloads/BNC_wordlists/e_lemma.txt
For the latter, you would probably want to generate a reverse dictionary mapping all the conjugated forms to their original (perhaps keeping in mind that there could be ambiguous forms, so make sure to check for these and handle them properly).
This might not be suitable in your specific solution but (for general knowledge) there is a great open-source software library called Spacy. It makes life easier in similar cases. To demonstrate:
texts = ["what's", "must've", "told"]
for text in texts:
doc = nlp(text)
lemmatized_text = ' '.join([token.lemma_ for token in doc])
print(lemmatized_text)
Outputs:
what be
must have
tell
