model performance 0.0023528579999999998
parsing model 0.0023423899999999998
mapping model 0.002284725
language tags 0.0022837969999999997
target language 0.002084604
average model 0.002076754
model score 0.002057915
model posterior 0.002034706
training data 0.00202925
source language 0.002010642
full model 0.0019367079999999999
petrov model 0.001935919
model petrov 0.001935919
model best 0.0019337479999999999
typological feature 0.0019268450000000002
such features 0.001905803
other languages 0.001899384
language tokens 0.001845664
target languages 0.001826184
new language 0.001800508
parallel data 0.00177168
language selection 0.0017591319999999998
source languages 0.001752222
feature description 0.0017483050000000001
typological features 0.001747475
pos tag 0.0017320109999999999
pos tags 0.001722049
poor language 0.0016903539999999998
model 0.00167232
chosen language 0.0016567139999999999
distributional features 0.001599573
different tags 0.00159261
syntactic transfer 0.0015767160000000001
test data 0.001554092
ical features 0.001551341
new languages 0.001542088
languages structure 0.001486597
syntactic parsing 0.001475687
feature 0.00147359
data requirement 0.001454963
transfer parser 0.0014417240000000001
dependency accuracy 0.001433957
universal tag 0.001432721
universal tags 0.001422759
gle languages 0.0014109
close languages 0.001405568
language 0.00140538
traget languages 0.001402664
prepositional languages 0.001399218
specific tags 0.001396031
universal pos 0.001387974
different parsing 0.0013842630000000002
pos annotation 0.001371153
parsing accuracy 0.0013607390000000001
tag statistics 0.001357724
other source 0.0013576859999999999
parsing performance 0.001350608
first parser 0.001341006
transfer method 0.001335082
direct transfer 0.001305558
features 0.00129422
mapping performance 0.001292943
multilingual dependency 0.001285427
pos tagset 0.001276593
tag sequences 0.001261289
transfer algorithm 0.001251035
gold tags 0.001250227
syntactic trans 0.001249502
common tags 0.001217716
gold pos 0.001215442
multilingual parsing 0.0012122090000000001
other properties 0.001205225
pos induction 0.001201124
noun verb 0.001200397
mixture parsing 0.001199322
manual pos 0.001188049
mapping optimization 0.001187722
czech tag 0.001182935
likely tag 0.001181133
general tag 0.0011796110000000001
mapping words 0.001178784
guage tag 0.001167839
target lan 0.001163915
available pos 0.0011561639999999999
rate tags 0.001155783
fine tags 0.001151043
japanese tag 0.0011498160000000001
parsing context 0.0011491890000000001
languages 0.00114696
mapping approach 0.001142799
tag diff 0.0011421320000000001
different mappings 0.001139554
noun phrase 0.001137023
syntactic trees 0.001135916
versal tags 0.0011330720000000002
ing accuracy 0.0011319049999999999
ing performance 0.0011217739999999999
pos annotations 0.001119422
syntactic category 0.001116738
goal noun 0.001115309
