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from __future__ import division
import math
MAXSCORE = 99
def EgivenF(fphrase, ephrase, paircount, fcount, fsample_count): # p(e|f)
return -math.log10(paircount/fcount)
def CountEF(fphrase, ephrase, paircount, fcount, fsample_count):
return math.log10(1 + paircount)
def SampleCountF(fphrase, ephrase, paircount, fcount, fsample_count):
return math.log10(1 + fsample_count)
def EgivenFCoherent(fphrase, ephrase, paircount, fcount, fsample_count):
prob = paircount/fsample_count
return -math.log10(prob) if prob > 0 else MAXSCORE
def CoherenceProb(fphrase, ephrase, paircount, fcount, fsample_count):
return -math.log10(fcount/fsample_count)
def MaxLexEgivenF(ttable):
def feature(fphrase, ephrase, paircount, fcount, fsample_count):
fwords = fphrase.words
fwords.append('NULL')
def score():
for e in ephrase.words:
maxScore = max(ttable.get_score(f, e, 0) for f in fwords)
yield -math.log10(maxScore) if maxScore > 0 else MAXSCORE
return sum(score())
return feature
def MaxLexFgivenE(ttable):
def feature(fphrase, ephrase, paircount, fcount, fsample_count):
ewords = ephrase.words
ewords.append('NULL')
def score():
for f in fphrase.words:
maxScore = max(ttable.get_score(f, e, 1) for e in ewords)
yield -math.log10(maxScore) if maxScore > 0 else MAXSCORE
return sum(score())
return feature
def IsSingletonF(fphrase, ephrase, paircount, fcount, fsample_count):
return (fcount == 1)
def IsSingletonFE(fphrase, ephrase, paircount, fcount, fsample_count):
return (paircount == 1)
def IsNotSingletonF(fphrase, ephrase, paircount, fcount, fsample_count):
return (fcount > 1)
def IsNotSingletonFE(fphrase, ephrase, paircount, fcount, fsample_count):
return (paircount > 1)
def IsFEGreaterThanZero(fphrase, ephrase, paircount, fcount, fsample_count):
return (paircount > 0.01)
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