parsing model 0.001818368
dependency model 0.001654768
model accuracy 0.0016369050000000001
accuracy model 0.0016369050000000001
training data 0.001567775
classifier model 0.001563308
different test 0.0015230830000000002
chinese parsing 0.001502166
different classifier 0.001497332
similar model 0.001482801
different classification 0.0014637640000000002
pcfg model 0.001397897
svm model 0.001389416
dependency parsing 0.0013728759999999999
different classes 0.0013671380000000002
dtree model 0.0013616980000000002
main data 0.001360087
parsing accuracy 0.001355013
maxent model 0.001347534
chinese dependency 0.001338566
ensemble model 0.00133147
deterministic model 0.001323774
ent model 0.001321906
parsing results 0.001319265
emulation model 0.001309531
model runtime 0.001297117
mbl model 0.001291136
sifier model 0.00128994
refined model 0.00128994
factored model 0.00128994
different classifiers 0.001274787
parser performance 0.0012729479999999999
same set 0.001250871
word length 0.0012499619999999999
classification parsing 0.001247848
treebank data 0.001241366
chinese text 0.00123806
word con 0.001233927
standard corpus 0.001220869
parsing task 0.001212105
current word 0.001211266
training set 0.001209505
parsing performance 0.001203917
additional information 0.001202813
syntactic parsing 0.001197109
test sentences 0.001193244
pcfg parser 0.001185036
svm parser 0.001176555
cal information 0.001172234
corpus preparation 0.0011543740000000001
shallow parsing 0.0011526779999999999
annotated data 0.0011442940000000001
pos tag 0.0011348180000000001
tic parser 0.001106117
chinese treebank 0.0010907360000000001
pos tags 0.0010900039999999999
constituent parsing 0.001085455
parsing process 0.001083023
istic parser 0.001081765
simple pos 0.0010793
ministic parser 0.001078597
parser computes 0.001078597
statistical parsing 0.0010694699999999999
classification accuracy 0.001066385
test set 0.0010652169999999998
parsing techniques 0.0010607569999999998
parsing framework 0.001058682
chinese constituent 0.001051145
model 0.00105013
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ing accuracy 0.001046253
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pos tagger 0.0010389589999999999
penn chinese 0.001029944
chinese family 0.001029317
accurate parsing 0.001029237
possible parse 0.001026257
constituency parsing 0.001019984
such pairs 0.001017587
mantic parsing 0.001014853
overall accuracy 0.001005031
standard training 0.001003902
time svm 9.949170000000001E-4
input sentence 9.93645E-4
linear time 9.91331E-4
pos tagging 9.89851E-4
chinese constituency 9.85674E-4
same collins 9.85658E-4
original training 9.80182E-4
lexical features 9.70444E-4
training examples 9.70261E-4
same rhyth 9.662E-4
other parsers 9.64994E-4
long sentences 9.53656E-4
pos errors 9.496659999999999E-4
many machine 9.45483E-4
feature set 9.41771E-4
entire training 9.40591E-4
first approach 9.40071E-4
binary features 9.38239E-4
