Question

我正在尝试从大型语料库中找到k个最常见的n-gram。我已经看到很多地方暗示了这种天真的方法 - 只需扫描整个语料库并保留所有n-gram数量的字典。有更好的方法吗？

Answer 1

在Python中，使用NLTK：

$ wget http://norvig.com/big.txt
$ python
>>> from collections import Counter
>>> from nltk import ngrams
>>> bigtxt = open('big.txt').read()
>>> ngram_counts = Counter(ngrams(bigtxt.split(), 2))
>>> ngram_counts.most_common(10)
[(('of', 'the'), 12422), (('in', 'the'), 5741), (('to', 'the'), 4333), (('and', 'the'), 3065), (('on', 'the'), 2214), (('at', 'the'), 1915), (('by', 'the'), 1863), (('from', 'the'), 1754), (('of', 'a'), 1700), (('with', 'the'), 1656)]

在Python中，原生（参见Fast/Optimize N-gram implementations in python）：

>>> def ngrams(text, n=2):
...     return zip(*[text[i:] for i in range(n)])
>>> ngram_counts = Counter(ngrams(bigtxt.split(), 2))
>>> ngram_counts.most_common(10)
    [(('of', 'the'), 12422), (('in', 'the'), 5741), (('to', 'the'), 4333), (('and', 'the'), 3065), (('on', 'the'), 2214), (('at', 'the'), 1915), (('by', 'the'), 1863), (('from', 'the'), 1754), (('of', 'a'), 1700), (('with', 'the'), 1656)]

在朱莉娅，请参阅Generate ngrams with Julia

import StatsBase: countmap
import Iterators: partition
bigtxt = readstring(open("big.txt"))
ngram_counts = countmap(collect(partition(split(bigtxt), 2, 1)))

粗略的时机：

$ time python ngram-test.py # With NLTK.

real    0m3.166s
user    0m2.274s
sys 0m0.528s

$ time python ngram-native-test.py 

real    0m1.521s
user    0m1.317s
sys 0m0.145s

$ time julia ngram-test.jl 

real    0m3.573s
user    0m3.188s
sys 0m0.306s

有没有更有效的方法来找到最常见的n-gram？

1 个答案: