correct tag 0.002788612
probability tag 0.002714825
single tag 0.0026508120000000002
tag sequence 0.002649779
individual tag 0.002606719
wordclass tag 0.002593502
tag names 0.002576257
tag pair 0.002571085
sible tag 0.002565589
likely tag 0.00255888
appropriate tag 0.002547621
probable tag 0.002546112
rect tag 0.00252502
suggested tag 0.0025211770000000003
opposing tag 0.0025211770000000003
corpus data 0.001990795
different data 0.00196393
training data 0.001943937
new tags 0.00177427
single tags 0.001764552
focus word 0.001762286
incorrect tags 0.0017354970000000001
individual tags 0.0017204590000000001
language model 0.001719942
tags sug 0.0016461940000000001
tial tags 0.001641716
ditto tags 0.001641591
tential tags 0.001638601
winning tags 0.001635292
learning system 0.001609482
correct tagging 0.001582352
extra data 0.001553122
entropy tagger 0.0015440599999999999
different features 0.001471349
specific tagging 0.001465027
lexical information 0.001463453
markov model 0.001456043
same corpus 0.00144655
context features 0.001422646
single tagger 0.001402922
same training 0.0013996920000000001
wordclass tagging 0.0013872420000000001
tagger combinations 0.001370217
unknown words 0.0013631709999999998
individual tagger 0.0013588289999999998
voting system 0.001354362
tagger agreement 0.0013433709999999999
driven model 0.001331013
mark tagging 0.001329344
tagger pair 0.0013231949999999999
tropy model 0.001320507
explicit model 0.001316803
benchmark tagging 0.0013150380000000001
feature values 0.001303738
tagger parameters 0.001287416
tagger construction 0.0012855199999999998
henceforth tagger 0.001277341
tagger gen 0.0012757519999999998
final system 0.0012651709999999998
second system 0.001256786
individual system 0.001256123
known words 0.001231698
third system 0.001227895
other nlp 0.001184152
basic corpus 0.0011830809999999999
other hand 0.001180676
feature infor 0.001180309
vidual system 0.001173806
mxpost system 0.00117234
contextual information 0.001171455
specific information 0.001154986
detailed information 0.001141338
treebank corpus 0.001133434
learning method 0.001133064
different errors 0.001130188
other tagsets 0.001113792
maximum entropy 0.001102077
machine learning 0.0011004370000000001
additional information 0.001099357
different methods 0.001096757
same nlp 0.001093733
several voting 0.00108675
corpus annotation 0.001079392
other vot 0.001072967
lexical statistics 0.001072122
information gain 0.001072093
different sys 0.001069793
case base 0.001063999
annotated training 0.0010631780000000001
model 0.0010474
tagging 0.00104501
english text 0.001039514
only information 0.001037324
case bases 0.001030466
language models 0.001029136
different agger 0.001022183
context probabilities 0.00102079
natural language 0.001020463
overall performance 0.0010180950000000001
training material 0.001017457
