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jacquard_vs_levenshtein.py
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jacquard_vs_levenshtein.py
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#Spelling Correction
# In[161]:
import nltk
from nltk.metrics import *
from nltk.util import ngrams
def jacquard_trigram(query):
final=[]
for a in file('enwiktionary.a.list'):
a=a.rstrip()
trigram=set(nltk.trigrams(a))
q_trigram=set(nltk.trigrams(query))
intersect=q_trigram.intersection(trigram)
union=q_trigram.union(trigram)
sim=float(len(intersect))/len(union)
final.append([a,sim])
final_sorted= sorted(final,key=lambda sim:sim[1], reverse=True)
print final_sorted[:10]
def jacquard_bigram(query):
final=[]
for a in file('enwiktionary.a.list'):
a=a.rstrip()
bigram=set(nltk.bigrams(a))
q_bigram=set(nltk.bigrams(query))
intersect=q_bigram.intersection(bigram)
union=q_bigram.union(bigram)
sim=float(len(intersect))/len(union)
final.append([a,sim])
final_sorted= sorted(final,key=lambda sim:sim[1], reverse=True)
print final_sorted[:10]
def jacquard_fourgram(query):
final=[]
n=4
for a in file('enwiktionary.a.list'):
a=a.rstrip()
fourgram=set(nltk.ngrams(a,4))
q_fourgram=set(nltk.ngrams(query,4))
intersect=q_fourgram.intersection(fourgram)
union=q_fourgram.union(fourgram)
sim=float(len(intersect))/len(union)
final.append([a,sim])
final_sorted= sorted(final,key=lambda sim:sim[1], reverse=True)
print final_sorted[:10]
def jacquard_fivegram(query):
final=[]
n=4
for a in file('enwiktionary.a.list'):
a=a.rstrip()
fivegram=set(nltk.ngrams(a,5))
q_fivegram=set(nltk.ngrams(query,5))
intersect=q_fivegram.intersection(fivegram)
union=q_fivegram.union(fivegram)
sim=float(len(intersect))/len(union)
final.append([a,sim])
final_sorted= sorted(final,key=lambda sim:sim[1], reverse=True)
print final_sorted[:10]
def leven_dist(word):
leven=[]
for a in file('enwiktionary.a.list'):
a=a.rstrip()
score=edit_distance(word,a)
leven.append([a,score])
leven_sorted= sorted(leven,key=lambda score:score[1])
print leven_sorted[:10]
# In[162]:
query=["abreviation", "abstrictiveness", "accanthopterigious", "artifitial inteligwnse", "agglumetation"]
for word in query:
print "The top ten words based on Jacquard similarity(5grams) of "+ word+ " are:\n"
jacquard_fivegram(word)
print "\n"
print "The top ten words based on Jacquard similarity(4grams) of "+ word+ " are:\n"
jacquard_fourgram(word)
print "\n"
print "The top ten words based on Jacquard similarity(trigrams) of "+ word+ " are:\n"
jacquard_trigram(word)
print "\n"
print "The top ten words based on Jacquard similarity(bigrams) of "+ word+ " are:\n"
jacquard_bigram(word)
print "\n"
print "The top ten words based on Levenshtein edit distance of "+ word+ " are:\n"
leven_dist(word)
print "\n"
# ### While comparing the jacquard similarities for various n grams it can be observed that bigrams give a better approximation.