python - 从句子中获取所有右分支词对

Question

鉴于我有一个像这样的字符串：

 'velvet evening purse bags'

我怎样才能得到这个的所有单词对？换句话说，所有这两个词的组合：

'velvet evening'
'velvet purse'
'velvet bags'
'evening purse'
'evening bags'
'purse bags'

我知道 python 的nltk包可以提供二元组，但我正在寻找超出该功能的东西。还是我必须用 Python 编写自己的自定义函数？

Answer 1

您可以itertools.combinations为此使用：

s = 'velvet evening purse bags'

from nltk import word_tokenize

words = word_tokenize(s)

from itertools import combinations

pairs = [' '.join(comb) for comb in combinations(words, 2)]

print(pairs)

输出：

['velvet evening', 'velvet purse', 'velvet bags', 'evening purse', 'evening bags', 'purse bags']

Answer 2

这应该很有趣=）

如果输入是velvet evening purse bags并且所需的输出是 @MrGeek 使用 产生itertools.combinations的，那实际上是skipgrams来自https://tedboy.github.io/nlps/generated/generated/nltk.skipgrams.html的定义

因此，您可以通过以下方式实现相同的目标：

from nltk import skipgrams

s = 'velvet evening purse bags'
tokens = word_tokenize(s)
list(skipgrams(tokens, n=2, k=len(tokens)-1))

[出去]：

[('velvet', 'evening'),
 ('velvet', 'purse'),
 ('velvet', 'bags'),
 ('evening', 'purse'),
 ('evening', 'bags'),
 ('purse', 'bags')]

在这种情况下，每个单词只能与它右侧的另一个单词组合；这有点符合人类的英语语言。

在这种情况下，单词的所有“排列”都会配对，甚至与它自己配对：

from itertools import product
s = 'velvet evening purse bags'
tokens = set(word_tokenize(s))
list(product(tokens, tokens))

[出去]：

[('velvet', 'velvet'),
 ('velvet', 'evening'),
 ('velvet', 'purse'),
 ('velvet', 'bags'),
 ('evening', 'velvet'),
 ('evening', 'evening'),
 ('evening', 'purse'),
 ('evening', 'bags'),
 ('purse', 'velvet'),
 ('purse', 'evening'),
 ('purse', 'purse'),
 ('purse', 'bags'),
 ('bags', 'velvet'),
 ('bags', 'evening'),
 ('bags', 'purse'),
 ('bags', 'bags')]

Answer 3

你也可以去老学校...

text =  'velvet evening purse bags'

n = []
ans = []
for i in text.split():
    for j in text.split():
        if j != i:
             if (i, j) not in n:
                ans.append((i, j))
                n.append((i, j))
                n.append((j, i))

输出

[('velvet', 'evening'),
 ('velvet', 'purse'),
 ('velvet', 'bags'),
 ('evening', 'purse'),
 ('evening', 'bags'),
 ('purse', 'bags')]