Python-给定2个句子字符串，计算余弦相似度

Question

从Python：tf-idf-cosine：查找文档相似度，可以使用tf-idf余弦计算文档相似度。如果不导入外部库，是否有任何方法可以计算2个字符串之间的余弦相似度？

s1 = "This is a foo bar sentence ."
s2 = "This sentence is similar to a foo bar sentence ."
s3 = "What is this string ? Totally not related to the other two lines ."

cosine_sim(s1, s2) # Should give high cosine similarity
cosine_sim(s1, s3) # Shouldn't give high cosine similarity value
cosine_sim(s2, s3) # Shouldn't give high cosine similarity value

Answer 1

一个简单的纯Python实现是：

import re, math
from collections import Counter

WORD = re.compile(r'\w+')

def get_cosine(vec1, vec2):
     intersection = set(vec1.keys()) & set(vec2.keys())
     numerator = sum([vec1[x] * vec2[x] for x in intersection])

     sum1 = sum([vec1[x]**2 for x in vec1.keys()])
     sum2 = sum([vec2[x]**2 for x in vec2.keys()])
     denominator = math.sqrt(sum1) * math.sqrt(sum2)

     if not denominator:
        return 0.0
     else:
        return float(numerator) / denominator

def text_to_vector(text):
     words = WORD.findall(text)
     return Counter(words)

text1 = 'This is a foo bar sentence .'
text2 = 'This sentence is similar to a foo bar sentence .'

vector1 = text_to_vector(text1)
vector2 = text_to_vector(text2)

cosine = get_cosine(vector1, vector2)

print 'Cosine:', cosine

印刷品：

Cosine: 0.861640436855

这里所用的余弦公式描述这里。

这不包括通过tf-idf对单词进行加权，但是为了使用tf-idf，你需要具有一个相当大的语料库才能从中估计tfidf的权重。

你还可以通过使用更复杂的方法从一段文本中提取单词，对其进行词干或词义化等来进一步开发它。