model model 0.00444998
tree model 0.003177611
joint model 0.003117737
pos tag 0.0028799
different pos 0.002701569
japanese word 0.002655195
english word 0.002634845
infinite model 0.0026051390000000002
target word 0.0025906809999999996
tion word 0.002587524
same pos 0.002573699
source word 0.002556395
word alignment 0.002541902
head word 0.002530284
children model 0.0025219780000000002
word unit 0.002495505
model ind 0.002491506
independent model 0.002490038
content word 0.002483243
pcfg model 0.00248231
pos tags 0.0024768999999999998
model gains 0.002470349
dependent model 0.002468555
pendent model 0.002466727
dent model 0.002465689
ing pos 0.002425178
possible pos 0.00241131
pos tagging 0.0023649739999999997
japanese pos 0.002276095
level pos 0.002261073
english pos 0.002255745
model 0.00222499
pos tagset 0.002213644
original pos 0.0022058909999999998
ipa pos 0.002185254
pos induction 0.002164505
pos refinement 0.002136132
separate pos 0.002134444
pos tagger 0.002132
pos tagsets 0.002123347
favorable pos 0.002102345
pos groups 0.002101708
other words 0.001812269
function words 0.001618698
dependency tree 0.001567651
english words 0.001527875
test data 0.0015270370000000002
other language 0.001502404
training data 0.001496626
target words 0.001483711
source words 0.0014494249999999998
head words 0.001423314
sentence data 0.001381233
infinite tree 0.00133277
sampling distribution 0.0013301699999999999
tag sets 0.001308933
prior distribution 0.001308152
tree structures 0.001282612
parse tree 0.001271285
finite tree 0.0012655359999999998
experimental data 0.001262366
dirichlet distribution 0.001256264
dependency parsing 0.001253636
data sparseness 0.001250439
possible state 0.001249183
development data 0.0012233320000000002
different instances 0.0012120920000000001
transition probability 0.001206937
sequential data 0.001196714
syntactic information 0.0011955310000000001
ment data 0.0011947
opment data 0.001194322
nite tree 0.0011933809999999999
different parent 0.001191858
ref joint 0.001187485
syntactic dependency 0.001185922
conditional distribution 0.001179711
state set 0.001174484
target language 0.0011738459999999999
language pairs 0.001164941
different observation 0.001163791
bayesian method 0.001154595
tion distribution 0.001153083
source language 0.00113956
words 0.00113354
observation distribution 0.0011297009999999999
tagging dependency 0.001118594
state variables 0.001114481
noun noun 0.001106984
whole state 0.001102201
other variables 0.001093927
dependency accuracy 0.00108709
emission distribution 0.001086743
different functions 0.001084108
state variable 0.001077064
distinct distribution 0.0010679729999999998
servation distribution 0.001063903
ditional distribution 0.0010633119999999999
same way 0.001057746
hidden state 0.001055094
