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event set 0.002296557
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event assignment 0.002088931
common event 0.002087215
dependency graph 0.002067813
level event 0.002061532
event zˆa 0.002047528
event assignments 0.002047113
dependency accuracy 0.002039109
dependency tree 0.002032398
rejection event 0.002032062
event vinea 0.00202632
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dependency structures 0.0020087209999999998
joint system 0.001897636
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dependency filters 0.001875795
feature vector 0.00185919
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training set 0.0018199269999999998
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event 0.00176717
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lsvm training 0.001597912
training objective 0.001576795
mst parser 0.001572058
unified training 0.001567808
training tech 0.001551199
training substan 0.001549196
parsing accuracy 0.0015464189999999998
joint classifier 0.001530915
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dependency 0.00147079
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features 0.00144229
joint weight 0.0014394870000000001
unfiltered parser 0.001439277
bust parser 0.001439277
same sentence 0.001397769
feature 0.00136215
parsing experiments 0.001355361
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vine parsing 0.001299832
training 0.00129054
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development data 0.001253688
same arc 0.001225615
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many arcs 0.0011839720000000001
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latent events 0.001141464
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linguistic structures 0.001069231
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same classi 0.001024317
multiple arcs 0.001012001
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inside events 0.001004629
token labels 0.001001007
joint 9.99831E-4
svm classifier 9.98293E-4
time memory 9.896380000000001E-4
same requirement 9.86449E-4
parsing 9.781E-4
classifier decisions 9.7585E-4
different tasks 9.65843E-4
total time 9.655130000000001E-4
model 9.55541E-4
token roles 9.51009E-4
other benefits 9.41597E-4
ing filter 9.408999999999999E-4
weight vector 9.366960000000001E-4
single arc 9.362169999999999E-4
previous work 9.301590000000001E-4
accurate parsers 9.22883E-4
multiple decisions 9.22197E-4
