与@larsman相同，但有一些预处理

import nltk, string
from sklearn.feature_extraction.text import TfidfVectorizer

nltk.download('punkt') # if necessary...


stemmer = nltk.stem.porter.PorterStemmer()
remove_punctuation_map = dict((ord(char), None) for char in string.punctuation)

def stem_tokens(tokens):
    return [stemmer.stem(item) for item in tokens]

'''remove punctuation, lowercase, stem'''
def normalize(text):
    return stem_tokens(nltk.word_tokenize(text.lower().translate(remove_punctuation_map)))

vectorizer = TfidfVectorizer(tokenizer=normalize, stop_words='english')

def cosine_sim(text1, text2):
    tfidf = vectorizer.fit_transform([text1, text2])
    return ((tfidf * tfidf.T).A)[0,1]


print cosine_sim('a little bird', 'a little bird')
print cosine_sim('a little bird', 'a little bird chirps')
print cosine_sim('a little bird', 'a big dog barks')

这里是Simphile NLP文本相似性Python包的创建者。Simphile包含几种文本相似度方法，它们与语言无关，并且比语言嵌入占用的cpu更少。

安装:

pip install simphile

选择你最喜欢的方法。这个例子显示了三点:

from simphile import jaccard_similarity, euclidian_similarity, compression_similarity

text_a = "I love dogs"
text_b = "I love cats"

print(f"Jaccard Similarity: {jaccard_similarity(text_a, text_b)}")
print(f"Euclidian Similarity: {euclidian_similarity(text_a, text_b)}")
print(f"Compression Similarity: {compression_similarity(text_a, text_b)}")

压缩相似性——利用压缩算法的模式识别 欧几里得相似性-把文本当作多维空间中的点，并计算它们的接近度 Jaccard Similairy -文字重叠越多，文本越相似

这里有一个小应用程序让你开始…

import difflib as dl

a = file('file').read()
b = file('file1').read()

sim = dl.get_close_matches

s = 0
wa = a.split()
wb = b.split()

for i in wa:
    if sim(i, wb):
        s += 1

n = float(s) / float(len(wa))
print '%d%% similarity' % int(n * 100)

如果您对测量两段文本的语义相似性更感兴趣，我建议您看看这个gitlab项目。你可以把它作为服务器运行，也有一个预先构建的模型，你可以很容易地使用它来测量两段文本的相似性;尽管它主要用于测量两个句子的相似度，但你仍然可以在你的情况下使用它。它是用java编写的，但您可以将其作为RESTful服务运行。

另一个选择是DKPro Similarity，这是一个库，有各种算法来测量文本的相似性。然而，它也是用java编写的。

代码示例:

// this similarity measure is defined in the dkpro.similarity.algorithms.lexical-asl package
// you need to add that to your .pom to make that example work
// there are some examples that should work out of the box in dkpro.similarity.example-gpl 
TextSimilarityMeasure measure = new WordNGramJaccardMeasure(3);    // Use word trigrams

String[] tokens1 = "This is a short example text .".split(" ");   
String[] tokens2 = "A short example text could look like that .".split(" ");

double score = measure.getSimilarity(tokens1, tokens2);

System.out.println("Similarity: " + score);

Generally a cosine similarity between two documents is used as a similarity measure of documents. In Java, you can use Lucene (if your collection is pretty large) or LingPipe to do this. The basic concept would be to count the terms in every document and calculate the dot product of the term vectors. The libraries do provide several improvements over this general approach, e.g. using inverse document frequencies and calculating tf-idf vectors. If you are looking to do something copmlex, LingPipe also provides methods to calculate LSA similarity between documents which gives better results than cosine similarity. For Python, you can use NLTK.

你可能想尝试一下cos文档相似度的在线服务http://www.scurtu.it/documentSimilarity.html

import urllib,urllib2
import json
API_URL="http://www.scurtu.it/apis/documentSimilarity"
inputDict={}
inputDict['doc1']='Document with some text'
inputDict['doc2']='Other document with some text'
params = urllib.urlencode(inputDict)    
f = urllib2.urlopen(API_URL, params)
response= f.read()
responseObject=json.loads(response)  
print responseObject