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')

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

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)

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.

为了用更少的数据集找到句子的相似性，并获得更高的精度，你可以使用下面的python包，它使用预训练的BERT模型，

pip install similar-sentences