Question

I need to get most popular ngrams from text. Ngrams length must be from 1 to 5 words.

I know how to get bigrams and trigrams. For example:

bigram_measures = nltk.collocations.BigramAssocMeasures()
finder = nltk.collocations.BigramCollocationFinder.from_words(words)
finder.apply_freq_filter(3)
finder.apply_word_filter(filter_stops)
matches1 = finder.nbest(bigram_measures.pmi, 20)

However, i found out that scikit-learn can get ngrams with various length. For example I can get ngrams with length from 1 to 5.

v = CountVectorizer(analyzer=WordNGramAnalyzer(min_n=1, max_n=5))

But WordNGramAnalyzer is now deprecated. My question is: How can i get N best word collocations from my text, with collocations length from 1 to 5. Also i need to get FreqList of this collocations/ngrams.

Can i do that with nltk/scikit ? I need to get combinations of ngrams with various lengths from one text ?

For example using NLTK bigrams and trigrams where many situations in which my trigrams include my bitgrams, or my trigrams are part of bigger 4-grams. For example:

bitgrams: hello my
trigrams: hello my name

I know how to exclude bigrams from trigrams, but i need better solutions.

Answer 1

update

Since scikit-learn 0.14 the format has changed to:

n_grams = CountVectorizer(ngram_range=(1, 5))

Full example:

test_str1 = "I need to get most popular ngrams from text. Ngrams length must be from 1 to 5 words."
test_str2 = "I know how to exclude bigrams from trigrams, but i need better solutions."

from sklearn.feature_extraction.text import CountVectorizer

c_vec = CountVectorizer(ngram_range=(1, 5))

# input to fit_transform() should be an iterable with strings
ngrams = c_vec.fit_transform([test_str1, test_str2])

# needs to happen after fit_transform()
vocab = c_vec.vocabulary_

count_values = ngrams.toarray().sum(axis=0)

# output n-grams
for ng_count, ng_text in sorted([(count_values[i],k) for k,i in vocab.items()], reverse=True):
    print(ng_count, ng_text)

which outputs the following (note that the word I is removed not because it’s a stopword (it’s not) but because of its length: https://stackoverflow.com/a/20743758/):

> (3, u'to')
> (3, u'from')
> (2, u'ngrams')
> (2, u'need')
> (1, u'words')
> (1, u'trigrams but need better solutions')
> (1, u'trigrams but need better')
...

This should/could be much simpler these days, imo. You can try things like textacy, but that can come with its own complications sometimes, like initializing a Doc, which doesn’t work currently with v.0.6.2 as shown on their docs. If doc initialization worked as promised, in theory the following would work (but it doesn’t):

test_str1 = "I need to get most popular ngrams from text. Ngrams length must be from 1 to 5 words."
test_str2 = "I know how to exclude bigrams from trigrams, but i need better solutions."

import textacy

# some version of the following line
doc = textacy.Doc([test_str1, test_str2])

ngrams = doc.to_bag_of_terms(ngrams={1, 5}, as_strings=True)
print(ngrams)

old answer

WordNGramAnalyzer is indeed deprecated since scikit-learn 0.11. Creating n-grams and getting term frequencies is now combined in sklearn.feature_extraction.text.CountVectorizer. You can create all n-grams ranging from 1 till 5 as follows:

n_grams = CountVectorizer(min_n=1, max_n=5)

More examples and information can be found in scikit-learn’s documentation about text feature extraction.