training data 0.0019285489999999999
learning algorithm 0.0017812689999999998
training set 0.001773845
training error 0.001609926
same training 0.001571481
test data 0.001486126
same data 0.00143941
training examples 0.001411766
full training 0.001378594
test set 0.001331422
algorithm adaboost 0.001330057
training sample 0.001325063
ith training 0.001294129
entire training 0.001270542
previous word 0.0012681769999999999
current word 0.001243765
ular training 0.001242613
next word 0.001233492
attachment data 0.001194587
test error 0.001167503
ing set 0.0011613180000000002
head word 0.00115377
computational learning 0.00115091
preceding word 0.00114508
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extreme data 0.001128578
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data fragmentation 0.001112538
tag local 0.001110072
simple features 0.001087216
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algorithm 0.00106746
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true tag 0.001045622
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training 0.00103031
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attribute set 0.001019933
development set 0.0010175750000000002
such rules 0.0010144630000000002
tial model 0.001008214
machine learning 0.001008011
ing error 9.97399E-4
test examples 9.693430000000001E-4
single tagger 9.65273E-4
sampling error 9.628970000000001E-4
separate tags 9.36599E-4
smoothing parameter 9.30242E-4
classification rule 9.28838E-4
learning theory 9.27532E-4
classification task 9.20251E-4
natural language 9.02101E-4
language processing 8.94028E-4
test sample 8.826400000000001E-4
error rate 8.80439E-4
previous results 8.789519999999999E-4
tagger predictions 8.70663E-4
multiple tags 8.633460000000001E-4
treebank corpus 8.61998E-4
classification problems 8.527420000000001E-4
overall test 8.508370000000001E-4
vbn test 8.50387E-4
corpus label 8.47048E-4
test vbp 8.449340000000001E-4
testing error 8.44514E-4
error sources 8.393610000000001E-4
new example 8.350170000000001E-4
tagger voting 8.27779E-4
language technology 8.25823E-4
man language 8.25823E-4
binary classification 8.23055E-4
simple rules 8.20493E-4
real number 8.202179999999999E-4
many pos 8.200880000000001E-4
classification problem 8.187579999999999E-4
large weight 8.164509999999999E-4
other taggers 8.14043E-4
accurate classification 8.0678E-4
error rates 8.05887E-4
improved test 8.00611E-4
model 7.95023E-4
final distribution 7.941070000000001E-4
error signif 7.92495E-4
appropriate number 7.913659999999999E-4
weak hypothesis 7.895269999999999E-4
true tags 7.82868E-4
linear function 7.78457E-4
other direction 7.77293E-4
maxent tagger 7.76931E-4
features 7.76255E-4
weak hypotheses 7.72702E-4
same space 7.71438E-4
probabilistic models 7.70742E-4
ent tagger 7.689299999999999E-4
positive number 7.68668E-4
known results 7.67831E-4
determinate tags 7.65429E-4
indefinite tags 7.65429E-4
fell number 7.59295E-4
