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training set 0.0026142509999999997
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gold training 0.0024444469999999998
wsj training 0.002399682
training use 0.002374207
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regular training 0.002303048
product model 0.002295988
supervised training 0.002246637
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data set 0.002207241
training distributions 0.002199774
training scenarios 0.0021971
different grammars 0.00212809
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ing data 0.002087453
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model selection 0.002050193
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treebank grammar 0.001999249
data sets 0.001961156
training 0.00194371
model rounds 0.001939287
model complexity 0.001938993
model combination 0.0019342600000000001
model com 0.001916399
bllip data 0.001817049
data sparsity 0.001790163
data spar 0.0017848270000000001
variable grammar 0.001779123
single grammar 0.001775345
product grammars 0.001756118
parser models 0.0017147809999999999
model 0.00164032
news grammars 0.001626352
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different time 0.001592293
grammar pool 0.001584067
gle grammar 0.0015794860000000002
wsj grammars 0.001556422
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regular grammars 0.001459788
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pcfg grammars 0.00144537
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parsing evaluation 0.0014329579999999998
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trained grammars 0.00139883
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grammars increases 0.001390522
newswire grammars 0.0013883300000000001
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component grammars 0.001380055
latent annotations 0.001379059
type parser 0.001376422
train models 0.001365888
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new parser 0.0013504699999999999
underlying grammars 0.001350266
struct grammars 0.001349144
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baseline parser 0.001310292
news test 0.001292833
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reranking parser 0.001267151
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wsj test 0.001222903
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feature sets 0.0012046259999999999
news treebank 0.001200891
automatic parse 0.0011661
tive parsing 0.00116599
ing features 0.00115715
parsing chart 0.00114179
wsj treebank 0.001130961
unlabeled sentences 0.001125885
standard product 0.00112195
