different model 0.0027452400000000004
pos tag 0.00273248
different pos 0.00253296
unsupervised pos 0.002140702
same pos 0.00209949
markov model 0.002082573
pos tags 0.001976064
graphical model 0.0019709750000000002
particular model 0.0019299870000000002
model choice 0.00192917
standard pos 0.001881757
prior distribution 0.001879632
same state 0.00187476
pos tagging 0.0018555910000000002
possible pos 0.001834528
other state 0.00182846
model capacity 0.0018172750000000001
model capacities 0.001810848
dirichlet distribution 0.00177963
different approach 0.0017463130000000002
different evaluation 0.001716143
joint distribution 0.0017102050000000002
tag sets 0.00168912
pos tagger 0.0016779310000000001
supervised pos 0.001670009
new state 0.001661193
appropriate pos 0.001649499
state sequence 0.0016487490000000001
mixture distribution 0.0016448410000000002
unique pos 0.001638752
actual tag 0.001637223
previous state 0.001631243
tag semantics 0.00163041
brill tag 0.00163041
real pos 0.0016292960000000001
annotated pos 0.001623796
actual pos 0.001605483
multinomial distribution 0.001571467
hidden state 0.001570858
model 0.00156265
different numbers 0.001536979
output distribution 0.0015278540000000001
state probabilities 0.001524998
different classes 0.001519224
specific distribution 0.001519193
initial state 0.001517813
different versions 0.001499196
particular state 0.001492977
state space 0.001487229
class distribution 0.00147936
different algo 0.0014762220000000001
state sequences 0.001468279
following distribution 0.001460431
different perspective 0.001455357
point distribution 0.001453988
different strategies 0.0014523420000000001
bayesian hmm 0.001440239
state vari 0.001438059
different authors 0.001435034
different behav 0.001432184
different biases 0.001432184
valid state 0.001431716
bayesian models 0.001428786
other words 0.001414271
discrete distribution 0.001385228
state identi 0.00137353
termediate distribution 0.001365057
unsupervised approach 0.001354055
same set 0.001352979
word types 0.0013354
hmm states 0.001322758
unsupervised systems 0.001322655
unsupervised markov 0.0013102550000000002
unsupervised learning 0.001306246
other states 0.001282029
mixture models 0.001260217
markov models 0.001252019
same cluster 0.0012391580000000002
unsupervised hidden 0.00123555
task data 0.001218799
same time 0.001210284
experimental results 0.001186213
same gold 0.0011823790000000001
dirichlet process 0.001160827
accuracy levels 0.001154334
dirichlet distributions 0.001149352
infinite hmm 0.001144765
same level 0.001143659
new transition 0.001127687
state 0.00112564
unsupervised approaches 0.001124687
poor results 0.001118546
distribution 0.00111672
output parameters 0.001112219
clustering evaluation 0.001110172
bayesian priors 0.001096741
bayesian methods 0.001079373
learning method 0.0010783659999999999
prior the 0.001076712
unlabeled data 0.001071603
