Fast n-gram calculation

I'm using NLTK to search for n-grams in a corpus but it's taking a very long time in some cases. I've noticed calculating n-grams isn't an uncommon feature in other packages (apparently Haystack has some functionality for it). Does this mean there's a开发者_运维百科 potentially faster way of finding n-grams in my corpus if I abandon NLTK? If so, what can I use to speed things up?

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Since you didn't indicate whether you want word or character-level n-grams, I'm just going to assume the former, without loss of generality.

I also assume you start with a list of tokens, represented by strings. What you can easily do is write n-gram extraction yourself.

def ngrams(tokens, MIN_N, MAX_N):
    n_tokens = len(tokens)
    for i in xrange(n_tokens):
        for j in xrange(i+MIN_N, min(n_tokens, i+MAX_N)+1):
            yield tokens[i:j]

Then replace the yield with the actual action you want to take on each n-gram (add it to a dict, store it in a database, whatever) to get rid of the generator overhead.

Finally, if it's really not fast enough, convert the above to Cython and compile it. Example using a defaultdict instead of yield:

def ngrams(tokens, int MIN_N, int MAX_N):
    cdef Py_ssize_t i, j, n_tokens

    count = defaultdict(int)

    join_spaces = " ".join

    n_tokens = len(tokens)
    for i in xrange(n_tokens):
        for j in xrange(i+MIN_N, min(n_tokens, i+MAX_N)+1):
            count[join_spaces(tokens[i:j])] += 1

    return count

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You might find a pythonic, elegant and fast ngram generation function using zip and splat (*) operator here :

def find_ngrams(input_list, n):
  return zip(*[input_list[i:] for i in range(n)])

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For character-level n-grams you could use the following function

def ngrams(text, n):
    n-=1
    return [text[i-n:i+1] for i,char in enumerate(text)][n:] 

---

def generate_ngrams(words, ngram=2):
  return [words[i:i+ngram] for i in range(len(words)-ngram+1)]



sentence = "I really like python, it's pretty awesome."
words = sentence.split()
words

['I', 'really', 'like', 'python,', "it's", 'pretty', 'awesome.']


res = generate_ngrams(words, ngram=2)
res

[['I', 'really'],
 ['really', 'like'],
 ['like', 'python,'],
 ['python,', "it's"],
 ["it's", 'pretty'],
 ['pretty', 'awesome.']]


res = generate_ngrams(words, ngram=3)
res

[['I', 'really', 'like'],
 ['really', 'like', 'python,'],
 ['like', 'python,', "it's"],
 ['python,', "it's", 'pretty'],
 ["it's", 'pretty', 'awesome.']]


res = generate_ngrams(words, ngram=4)
res

[['I', 'really', 'like', 'python,'],
 ['really', 'like', 'python,', "it's"],
 ['like', 'python,', "it's", 'pretty'],
 ['python,', "it's", 'pretty', 'awesome.']]