我正在尝试从一个大语料库中找出前 k 个最常见的 n-grams。我看到很多地方都建议使用朴素的方法 - 简单地扫描整个语料库并保留所有 n-grams 的计数字典。有没有更好的方法来做这件事?
我正在尝试从一个大语料库中找出前 k 个最常见的 n-grams。我看到很多地方都建议使用朴素的方法 - 简单地扫描整个语料库并保留所有 n-grams 的计数字典。有没有更好的方法来做这件事?
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,原生支持N-gram算法(参见Python中快速/优化的N-gram实现):
>>> import collections
>>> def ngrams(text, n=2):
... return zip(*[text[i:] for i in range(n)])
>>> ngram_counts = collections.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Julia中,参见使用Julia生成ngram。import StatsBase: countmap
import Iterators: partition
bigtxt = readstring(open("big.txt"))
ngram_counts = countmap(collect(partition(split(bigtxt), 2, 1)))
大致时间:
$ 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