word sense 0.00534659
sense disambiguation 0.004267244
first sense 0.004234083
sense correct 0.004024746
sense distribution 0.003992663
sense estimation 0.0039415349999999995
predominant sense 0.003934523
candidate sense 0.003905795
sense inventory 0.003899314
sense heuristic 0.003895222
cat sense 0.003880321
sense heuristics 0.003879536
sense idss 0.003860329
sense international 0.0038515999999999997
sense 0.00360402
word senses 0.00327588
noun senses 0.002258891
target word 0.002196748
word replacement 0.00200827
many senses 0.002002234
noun words 0.001939251
average senses 0.0018952399999999999
candidate senses 0.001835085
senses prec 0.001828788
appropriate senses 0.001824848
senses cat 0.001809611
tween senses 0.0017717969999999999
nant senses 0.001770288
senses accord 0.001769595
senses 0.00153331
cat words 0.001489971
unknown words 0.001462903
wsd task 0.001419296
training data 0.001396498
similarity package 0.001389589
wsd systems 0.0013838280000000001
test data 0.0013706909999999998
net similarity 0.001344643
supervised wsd 0.001341984
training set 0.001331432
test set 0.001305625
english data 0.001304396
reuters corpus 0.0012428069999999999
domain set 0.001241169
semcor corpus 0.001234133
words 0.00121367
journal corpus 0.001204983
textual corpus 0.001204498
edr corpus 0.001193827
frequency data 0.001188864
brown corpus 0.001184483
dso corpus 0.001177103
corpus statis 0.001177103
corpus hav 0.001177103
different lan 0.001172328
different predomi 0.001131828
similarity 0.00109412
classification performance 0.001079371
text classification 0.001077963
english results 0.0010551129999999999
semcor data 0.001042008
japanese data 0.001033666
data sets 0.001029327
classification accuracy 0.0010171540000000001
unlabeled data 0.0010157439999999998
matrix form 0.001006881
noun hierarchy 0.001002569
svm model 9.94307E-4
score score 9.8906E-4
mrw model 9.68201E-4
inant noun 9.64467E-4
training documents 9.63014E-4
supervised domain 9.58211E-4
similar distribution 9.523889999999999E-4
average number 9.48762E-4
first step 9.41627E-4
corpus 9.40028E-4
test documents 9.37207E-4
specific domain 9.237990000000001E-4
new domain 9.2228E-4
train test 9.1975E-4
target annotation 9.15148E-4
domain labels 9.15007E-4
wordnet synsets 9.04717E-4
test sets 9.042119999999999E-4
many nlp 9.027729999999999E-4
domain list 8.96573E-4
total number 8.89567E-4
idss first 8.863720000000001E-4
overall performance 8.72698E-4
future work 8.72671E-4
ing vector 8.67879E-4
active learning 8.51183E-4
sufficient number 8.47155E-4
limited number 8.32617E-4
same period 8.29482E-4
text classi 8.2804E-4
text classifi 8.228230000000001E-4
domain adaptation 8.08978E-4
same ratio 7.857189999999999E-4
