#用向量空间模型计算两个字符串s和s1之间的相似度
from math import sqrt
from collections import Counter
import re
def vsm_distance(s,s1):
#将s,s1转化为字典格式（dictionary{词：词频}）
mylist=re.findall(r"\w+",s)
ss=Counter( mylist)
mylist1=re.findall(r"\w+",s1)
ss1=Counter( mylist1)
#向量空间计算
c = set(ss.keys())&set(ss1.keys())
if not c:
return 0
x = sum([ss.get(i)*ss1.get(i) for i in c])
sq1 = sqrt(sum([pow(ss.get(i),2) for i in ss.values()]))
sq2 = sqrt(sum([pow(ss1.get(i),2) for i in ss1.values()]))
p = float(x)/(sq1*sq2)
return p
s="KBA is to give a chance to non-popular entities information to be updated as soon as a useful information is published on the internet. The KBA organizershave built up a stream-corpus which is a huge corpus of timestamped web documents that can be processed chronologically. Hence it is possible to simulate a real time system. The documents come from newswires, blogs, forums, review, memetracker….. In addition, a set of target entities, coming from wikipedia or from twitter, has been selected for their ambiguity or unpopularity. And last but not least, more than 60000 documents have been annotated so that systems can train on it. The train period starts on documents published from october 2011 until februray, and the test period starts from februray 2012 to februray 2013."
s1="The KBA track is divided in two tasks:CCR(Cumulative Citation Recommendation) and SSF(Streaming Slot Filling). CCR task is to filter out documents worth citing in a profile of an entity(e.g., wikipedia or freebase article). SSF task is to detect changes on given slots for each of the target entities. This article is focused only on CCR task."
vsm_distance(s,s1)
