# 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)
unknowntext = """Yesterday we scored ten goals in the last 45 minutest of the game."""
"""
Wimbledon 2013: Andy Murray's victory could boost British tennis
Comments (149)
All too often sporting moments and achievements are given a misplaced historical significance. Not on Sunday.
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 )
print("young Fisherman", lhr, pmi, sep="\t")
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)
print("young Fisherman", lhr, pmi, sep="\t")
# iron chain: likelihood ratio
