sense sense 0.00550144
word sense 0.003651797
sense baseline 0.003428495
semantic features 0.00342335
sense entropy 0.003254605
large sense 0.003253184
sense annotation 0.003237812
sense number 0.003221366
single sense 0.003217889
sense frequency 0.003207537
high sense 0.003204504
sense disambiguation 0.003181469
correct sense 0.003158467
sense distribution 0.0030832209999999997
sense distinctions 0.0030802769999999998
frequent sense 0.003050536
dominant sense 0.003034808
different features 0.00303401
ith sense 0.003033883
sense 0.00275072
syntactic features 0.0025858
role features 0.0025388679999999997
such features 0.002524638
linguistic features 0.002406935
semantic role 0.0023148780000000002
frequency features 0.002280487
local features 0.002224971
feature selection 0.002221851
feature performance 0.002200791
related features 0.00217971
different verbs 0.0021602130000000002
collocation features 0.002159159
discriminative features 0.0021256639999999998
different word 0.002111417
following features 0.0021037309999999997
different words 0.002103437
motivated features 0.002098374
semantic category 0.002094924
topical features 0.002092461
discourse features 0.002089202
automatic feature 0.002069763
collocation feature 0.0020365590000000003
discriminative feature 0.002003064
semantic categories 0.001995309
important feature 0.001993272
semantic relations 0.001976755
feature design 0.001972935
feature independence 0.001967213
syntactic information 0.0019316399999999999
semantic dictionaries 0.001909534
role information 0.001884708
features 0.00182367
english data 0.0017691830000000001
linguistic information 0.0017527749999999998
test data 0.0017120260000000002
word senses 0.001702705
feature 0.00170107
other verbs 0.001691946
training data 0.001646941
frequency information 0.001626327
learning model 0.001608244
different window 0.001596338
data set 0.001592013
english verbs 0.0015771359999999998
different types 0.001525342
new data 0.001524255
chinese verbs 0.001518158
speech information 0.001500906
different reasons 0.001493533
different properties 0.0014868070000000001
different dimensions 0.0014859130000000002
different sizes 0.00148541
different criteria 0.0014840740000000002
labeling information 0.0014782329999999998
additional data 0.0014777100000000001
available data 0.001476709
information science 0.001475988
chinese word 0.001469362
chinese words 0.001461382
information benefits 0.0014470109999999998
the data 0.001432052
wsd system 0.0013566469999999999
english wsd 0.001350946
bayesian model 0.001337782
individual verbs 0.001333845
target verbs 0.001311664
category senses 0.0012968720000000001
word categories 0.001296706
local words 0.001294398
chinese wsd 0.0012919680000000001
parsing model 0.001273936
target word 0.0012628679999999999
english system 0.001260227
frequency senses 0.001258445
syntactic category 0.001257374
particular verbs 0.0012541459999999998
head word 0.001250495
difficult verbs 0.001241192
next word 0.001241186
wsd accuracy 0.0012350199999999999
