word sense 0.00416966
wordnet sense 0.0030938610000000003
sense information 0.002931062
work sense 0.002923558
sense disambiguation 0.002922608
same sense 0.00290276
word similarity 0.00283131
method sense 0.00278168
word senses 0.002697875
unwanted sense 0.002618754
sense dis 0.0026087510000000003
sense handling 0.0026017980000000002
implicit sense 0.00259839
sense hierarchies 0.0025954000000000003
sense decisions 0.002585743
wordnet word 0.002582221
sense invento 0.002579898
sense 0.00234065
words model 0.002211961
word relationships 0.0022080240000000003
word plant 0.002196382
corresponding word 0.002171933
target word 0.00215206
distributional word 0.002151597
word vectors 0.002139259
proper word 0.002099379
implicit word 0.00208675
absolute word 0.002080752
word vec 0.002078361
explicit word 0.002078317
biguous word 0.002072159
preceding word 0.002070965
plicit word 0.002068229
uncontextualized word 0.002068229
training data 0.001874989
different similarity 0.0018173619999999999
quadruple words 0.001767827
system context 0.001635892
wsd methods 0.001604824
disambiguation system 0.001602618
wsd algorithm 0.001524079
distributional data 0.001521477
training set 0.001513822
tree system 0.0014948280000000001
several similarity 0.001491006
semantic relations 0.001490649
data points 0.001486616
words 0.00147083
data sparsity 0.001450858
cific data 0.001445212
butional data 0.001440685
combat data 0.001438391
ing wsd 0.001431239
test set 0.001409061
similarity measure 0.001407216
full wsd 0.001397663
training corpus 0.001387227
entire system 0.0013785730000000001
baseline wsd 0.001359837
neighbor system 0.001354127
semantic relatedness 0.0013256449999999999
different vector 0.001322342
quadruple similarity 0.001299297
similarity measures 0.001298293
tion system 0.001296971
accurate system 0.0012962450000000001
test corpus 0.001282466
cosine similarity 0.001272608
system construct 0.001267937
wordnet approach 0.001267065
approach wordnet 0.001267065
nal system 0.001265777
similarity compar 0.0012456540000000001
similarity mea 0.001245009
sine similarity 0.00124448
syntactic model 0.00122523
classifier accuracy 0.0012169989999999999
wsd module 0.001211322
explicit wsd 0.001206726
context information 0.001205644
different dataset 0.001201571
similar verbs 0.001199444
wsd modules 0.001198429
attachment disambiguation 0.001175931
human performance 0.0011644429999999998
other systems 0.001160817
learning algorithm 0.0011586679999999999
same attachment 0.001156083
different prepositions 0.0011507359999999999
training examples 0.0011385710000000001
value accuracy 0.001133773
space model 0.0011308730000000001
all senses 0.001128236
context vector 0.001122512
high performance 0.00112207
standard corpus 0.00112123
disambiguation systems 0.001106138
unsupervised corpus 0.001105341
different ways 0.001098574
results table 0.001088014
