language data 0.0027320499999999998
language pos 0.0025542860000000002
other language 0.002327795
supervised model 0.0022960890000000003
unsupervised model 0.002244902
pos tag 0.0022212760000000003
model parameters 0.002153083
induction model 0.0021129630000000003
hmm model 0.002107194
emission model 0.0021026490000000003
final model 0.00209748
english word 0.0020490960000000003
pervised model 0.002037077
unsupervised word 0.002036102
target language 0.0020348750000000002
prediction model 0.0020282950000000003
word type 0.002022803
markov model 0.002020104
foreign language 0.002019396
standard word 0.002019387
language vertices 0.001998776
pos tags 0.001980786
full model 0.001976002
ibm model 0.0019679700000000003
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foreign word 0.001935016
language taggers 0.00192376
language pairs 0.001918913
right word 0.001910143
training data 0.00190983
word alignment 0.0018861239999999999
language trigrams 0.001884793
language side 0.001884171
word alignments 0.001867618
language translations 0.001862884
eign language 0.00184889
word types 0.001817221
center word 0.001805068
word align 0.001774046
middle word 0.001773549
individual word 0.001769442
eign word 0.00176451
tag distributions 0.001762427
word identity 0.001762091
different feature 0.001760595
word fidanzato 0.001758047
tag distribution 0.001750753
model 0.00172206
tag probabilities 0.0016369090000000002
parallel data 0.001619848
projected tag 0.0016132920000000001
structured data 0.001610443
pos information 0.001608669
different languages 0.001601182
language 0.00159764
different label 0.0015775540000000001
english tags 0.001559976
foreign data 0.0015561659999999999
treebank data 0.001555271
correct tag 0.001554898
frequent tag 0.001550106
bilingual pos 0.001549926
supervised pos 0.001530675
labeled data 0.001510618
pute tag 0.001509023
annotated data 0.001501194
english pos 0.001492482
unsupervised pos 0.0014794880000000002
possible tags 0.001461021
pos distributions 0.0014544430000000001
universal tags 0.001451941
pos tagger 0.0014502809999999999
treebank tags 0.001445001
pos tagset 0.001428938
graph label 0.001421833
pos tagging 0.001419719
average pos 0.001406749
other words 0.001402606
universal pos 0.001384447
pos labels 0.001377128
specific pos 0.001363367
feature trigram 0.001361819
different models 0.00135441
supervised training 0.001349449
gold tags 0.001348886
pos induction 0.001347549
same label 0.0013380599999999999
constraint feature 0.001309331
fine tags 0.0013024619999999999
pos taggers 0.001282766
pos projection 0.0012805
versal tags 0.001271223
italicized tags 0.001268794
other models 0.001261776
bilingual graph 0.0012603480000000001
text words 0.001256406
label distributions 0.001252562
feature value 0.001252253
accurate pos 0.001245767
bilingual information 0.001245303
