training data 0.0036173200000000003
training algorithm 0.00301801
unlabeled training 0.00287692
unlabeled data 0.00284688
training test 0.002602478
supervised training 0.002549818
training approach 0.00246408
labeled training 0.002403908
training algorithms 0.002381426
labeled data 0.002373868
training set 0.002369361
dependency parsing 0.0023549499999999998
standard training 0.002292394
training sentences 0.0022726350000000003
convex training 0.002263183
unsupervised data 0.002204158
dependency models 0.002198817
training problem 0.002191981
margin training 0.002184235
training algo 0.002184067
training procedure 0.002177584
training approaches 0.002171144
efficient training 0.002164336
experimental data 0.0021087379999999998
unannotated data 0.002093216
training sen 0.002088347
training category 0.002084666
straightforward training 0.002078936
training tech 0.002078936
annotated data 0.002070358
data source 0.002069315
design data 0.002058715
data part 0.0020536870000000002
data resource 0.002048482
learning algorithm 0.002045366
parsing models 0.001992947
dependency tree 0.001965741
parsing model 0.001949237
dependency parser 0.001939429
supervised algorithm 0.0019204679999999998
tree algorithm 0.001879661
training 0.00182368
dependency trees 0.001781015
unlabeled sentence 0.001762982
parsing results 0.001745959
features score 0.001737529
directed dependency 0.001708958
dependency parsers 0.001669159
corresponding dependency 0.0016553280000000002
dependency structure 0.001641429
parsing algorithms 0.001632286
new algorithm 0.001625407
simple features 0.001616432
loss function 0.001587675
indicator features 0.001586379
supervised learning 0.001577174
dependency relations 0.0015768520000000001
dependency pars 0.0015565590000000001
dependency links 0.0015525860000000001
glish dependency 0.001539414
arbitrary features 0.001538645
rected dependency 0.001537266
bilistic dependency 0.00153539
language parsing 0.001513024
feature set 0.0015123419999999999
descent algorithm 0.00149816
feature vector 0.001487455
novel algorithm 0.001481829
parsing accuracy 0.0014794749999999998
parsing problem 0.0014428409999999998
learning algorithms 0.001408782
supervised results 0.001397557
unlabeled corpora 0.001368023
head word 0.001366749
supervised approach 0.001366538
generative models 0.001364946
structured loss 0.001341895
parsing accu 0.001338099
real word 0.001337612
score function 0.00132525
objective function 0.0013211289999999999
child word 0.001319766
model parameters 0.001307398
word pair 0.001299593
language learning 0.00128952
dependency 0.00128041
features 0.00127513
parameter estimation 0.0012646649999999999
unsupervised learning 0.001261554
distance function 0.001239585
machine learning 0.001226785
learning tasks 0.001224552
margin learning 0.001211591
test sets 0.001206428
algorithm 0.00119433
tree ing 0.0011905079999999998
convex loss 0.001164327
chain model 0.001146739
supervised methods 0.001146643
gaussian function 0.0011401200000000001
