from corpus import loadTextFromFile, getNGrams, getTokenCounts, prettyPrintFrequencyProfile, relativizeTokenCounts, tokenize
from math import log
def getMIScore(bigramprob, unigramprobaA, unigramprobB):
return bigramprob * log(bigramprob / (unigramprobaA * unigramprobB) )
def getMIScoreFromFQP( bigrams, unigrams, bigram):
tokenA, tokenB = bigram.split()
return bigrams[bigram] * log( bigrams[bigram] / (unigrams[tokenA] * unigrams[tokenB]) )
tokens = tokenize( loadTextFromFile("pg873.txt") )
unigrams = getNGrams(tokens, 1)
relativizeTokenCounts( unigrams )
bigrams = getNGrams(tokens, 2)
#prettyPrintFrequencyProfile(bigrams, myreverse=False)
relativizeTokenCounts( bigrams )
# young King: likelihood ratio
lhr = bigrams["young Fisherman"] / (unigrams["young"] * unigrams["Fisherman"])
# young King - pointwise Mutual Information
pmi = bigrams["young Fisherman"] * log( lhr )
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import sys, codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.detach())
from corpus import loadTextFromFile, tokenize, getNGrams
myngrams = getNGrams(tokenize( loadTextFromFile("bbc-1.txt") ))
try:
outfile = open("test2.dot", mode="w", encoding="utf-8")
print("digraph g {", file=outfile)
for bigram in myngrams:
tokens = bigram.split()
print('"' + tokens[0] + "\" -> \"" + tokens[1] + "\";", file=outfile)
print("}", file=outfile)
outfile.close()
except IOError:
pass
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import sys, codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.detach())
from corpus import loadTextFromFile, tokenize, getNGrams
mytext = loadTextFromFile("cnn-1.txt")
mytokens = tokenize(mytext)
myngrams = getNGrams(mytokens, 2)
try:
outputfile = open("sample-a1.xml", mode='w', encoding='utf-8')
print("<bigrams>", file=outputfile)
for bigram in myngrams:
lefttoken, righttoken = bigram.split()
print("<bigram>", file=outputfile)
print("<lefttoken>" + lefttoken + "</lefttoken>", file=outputfile)
print("<righttoken>" + righttoken + "</righttoken>", file=outputfile)
print("<frequency>" + str(myngrams[bigram]) + "</frequency>", file=outputfile)
print("</bigram>", file=outputfile)
print("</bigrams>", file=outputfile)
outputfile.close()
except IOError:
print("Cannot open file...")
#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import sys, codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.detach())
from corpus import loadTextFromFile, tokenize, getNGrams
myngrams = getNGrams(tokenize(loadTextFromFile("bbc-1.txt")))
try:
outfile = open("test2.dot", mode="w", encoding="utf-8")
print("digraph g {", file=outfile)
for bigram in myngrams:
tokens = bigram.split()
print('"' + tokens[0] + "\" -> \"" + tokens[1] + "\";", file=outfile)
print("}", file=outfile)
outfile.close()
except IOError:
pass
# -*- coding: UTF-8 -*-
import sys, codecs
sys.stdout = codecs.getwriter('utf-8')(sys.stdout.detach())
# use this and then prefix all function names with corpus.
#import corpus
# or use this
from corpus import loadTextFromFile, tokenize, getTokenCounts, prettyPrintFrequencyProfile, relativizeTokenCounts
from math import log
mytext = loadTextFromFile("pg873.txt")
# tokenize mytext and return list of tokens
tokens = tokenize(mytext)
# count tokens
mydict = getTokenCounts(tokens)
relativizeTokenCounts(mydict)
# pretty-print tokens and frequencies
#prettyPrintFrequencyProfile(mydict, sortbyfrq=True, myreverse=True)
mytext = loadTextFromFile("sports-bbc.txt")
mysportsdict = getTokenCounts(tokenize(mytext))
relativizeTokenCounts(mysportsdict)
from corpus import loadTextFromFile, getNGrams, getTokenCounts, prettyPrintFrequencyProfile, relativizeTokenCounts, tokenize
from math import log
def getMIScore(bigramprob, unigramprobaA, unigramprobB):
return bigramprob * log(bigramprob / (unigramprobaA * unigramprobB))
def getMIScoreFromFQP(bigrams, unigrams, bigram):
tokenA, tokenB = bigram.split()
return bigrams[bigram] * log(bigrams[bigram] /
(unigrams[tokenA] * unigrams[tokenB]))
tokens = tokenize(loadTextFromFile("pg873.txt"))
unigrams = getNGrams(tokens, 1)
relativizeTokenCounts(unigrams)
bigrams = getNGrams(tokens, 2)
#prettyPrintFrequencyProfile(bigrams, myreverse=False)
relativizeTokenCounts(bigrams)
# young King: likelihood ratio
lhr = bigrams["young Fisherman"] / (unigrams["young"] * unigrams["Fisherman"])
# young King - pointwise Mutual Information
pmi = bigrams["young Fisherman"] * log(lhr)
