pos word 0.004891970000000001
word word 0.00485964
pos tag 0.00368339
pos features 0.00348269
pos feature 0.003223203
standard pos 0.0031845420000000003
pos tagger 0.0031731980000000003
pos tagging 0.0031474660000000002
pos ambiguity 0.0031411240000000003
word accuracy 0.003122387
pos tags 0.00308784
method pos 0.003005619
many pos 0.002935566
ing pos 0.0028919870000000004
pos level 0.002891755
pos sequence 0.0028373160000000003
correct pos 0.00283144
current word 0.002813051
pos gold 0.00281249
gold pos 0.00281249
additional pos 0.0028011760000000003
multiple pos 0.00280035
extra pos 0.00279546
pos taggers 0.0027895180000000004
treebank pos 0.0027708140000000003
pos column 0.0027622470000000002
particular word 0.0027456950000000003
tiple pos 0.002730095
pos multi 0.0027174390000000003
multi pos 0.0027174390000000003
sent word 0.002716145
word sent 0.002716145
auto pos 0.0027152
pos auto 0.0027152
pos amb 0.002715135
assigned pos 0.002709169
ith word 0.002684807
word acc 0.0026844720000000002
amb word 0.002682805
parsing model 0.002268216
tag accuracy 0.001913807
supertagging model 0.00191368
tag ambiguity 0.001900214
tag probability 0.001899977
training data 0.001809311
ccg tag 0.0017965479999999998
markov model 0.001767214
entropy model 0.001762891
tag set 0.0017559759999999998
model generalises 0.001710226
discretized model 0.001710226
tag supertagger 0.001652041
parsing parsing 0.001610692
example tag 0.001604799
correct tag 0.00159053
category features 0.001584147
extra tag 0.00155455
ccg parser 0.0015519700000000002
tag decision 0.001536948
tag probabilities 0.0015323519999999998
probability information 0.001519169
parser evaluation 0.0015143090000000001
alternative tag 0.001505958
vidual tag 0.001469509
tag uncertainty 0.0014683979999999999
test data 0.0014675460000000001
model 0.00146287
standard approach 0.001452351
lexical category 0.001429839
ccg parsing 0.0013806539999999998
tagging accuracy 0.001377883
sentence accuracy 0.001376243
initial words 0.001370607
binary features 0.0013493589999999998
syntactic ambiguity 0.001324178
other tagging 0.001320085
unseen data 0.001314365
training set 0.001298077
ment data 0.001295796
particular parser 0.001292537
important features 0.0012852929999999999
portant features 0.0012783859999999998
informative features 0.0012684019999999999
accurate parser 0.001247512
category ambiguity 0.001242581
rasp parser 0.0012252720000000001
large corpus 0.001220313
first approach 0.001208942
lexical categories 0.001197617
new approach 0.001187301
models maximum 0.0011779870000000001
feature values 0.00116639
statistical models 0.001161237
full parsing 0.001158979
supertagging accuracy 0.0011433770000000001
entropy models 0.0011398
lexical cate 0.001133739
parsing process 0.001128855
statistical parsing 0.001126804
similar results 0.001123859
