language model 0.00352816
different language 0.0033139
accuracy language 0.0029124019999999997
language learning 0.0028509349999999998
different pos 0.00279376
pos tag 0.00279031
different word 0.00271661
language identification 0.002711195
language models 0.002707184
pos tags 0.00269455
human language 0.002614166
pos tagger 0.0025965090000000003
particular language 0.00258123
level language 0.002528181
character language 0.002489826
language alternation 0.002476358
language pairs 0.002461675
language combi 0.0024431789999999998
english pos 0.002438928
automated language 0.002428838
complex language 0.002426074
language identifica 0.002415262
dominant language 0.0024135289999999998
language mixtures 0.0024135289999999998
language transitions 0.0024135289999999998
language identifi 0.0024135289999999998
language identi 0.0024135289999999998
sisted language 0.0024135289999999998
official language 0.0024135289999999998
english word 0.002361778
spanish pos 0.0021878
lexical word 0.002171557
language 0.00217097
pos tagging 0.0021314430000000002
monolingual pos 0.0020921810000000002
pos taggers 0.002016561
high pos 0.002009807
right pos 0.001977538
ferent pos 0.001954583
crf pos 0.001952204
corresponding pos 0.001946756
simple word 0.0019372360000000002
word form 0.001930893
word level 0.0019308910000000001
final pos 0.001923766
automated pos 0.0019086980000000001
word boundaries 0.001889821
full word 0.001878112
different learning 0.001822895
word contractions 0.001821255
tag information 0.001796749
tag set 0.001770291
english tagger 0.0017337770000000001
spanish tag 0.00167645
production model 0.001641312
markov model 0.001638789
model orders 0.0016005720000000002
sequential model 0.0016005720000000002
monolingual tag 0.001580831
spanish tags 0.00158069
different results 0.0015597179999999999
different methods 0.00155967
particular tag 0.00154974
different approaches 0.001522575
spanish tagger 0.001482649
treebank tag 0.001470556
right tag 0.001466188
different machine 0.0014565289999999998
different speakers 0.001448493
different back 0.0014441699999999998
tag sets 0.00144007
different heuristics 0.001437528
different tagset 0.0014287409999999999
final tag 0.001412416
different annotator 0.001411068
special tag 0.001397846
different amounts 0.001395959
different strategies 0.0013921049999999998
different sub 0.0013916459999999999
monolingual tagger 0.00138703
different subsets 0.001386514
different learners 0.001386514
different portions 0.001386514
guage tags 0.001364934
model 0.00135719
first approach 0.00134976
training data 0.0013483570000000001
learning approach 0.001320565
data set 0.0013147179999999999
special tags 0.001302086
english dictionary 0.001299773
training set 0.001295261
foreign words 0.0012951619999999999
identification accuracy 0.0012816569999999998
tree tagger 0.001274614
tagger confidence 0.001257008
learning algorithm 0.001254118
contextual features 0.00124377
corresponding tagger 0.001241605
tagger output 0.001233475
