word sense 0.00277582
word vectors 0.002157451
clustering algorithm 0.002071501
first word 0.002051285
second word 0.0019651
name data 0.0019274700000000001
target word 0.001896414
pervised word 0.0018217300000000001
stop word 0.001803088
word ergonomics 0.001801167
sense disambiguation 0.001774619
test data 0.001738406
significant words 0.001684835
good data 0.001649851
feature vector 0.001647574
clustering performance 0.001642429
context vector 0.001642208
sense discrimination 0.001606117
bush cluster 0.001602483
experimental data 0.00160245
labels cluster 0.001593284
cluster labels 0.001593284
selection data 0.001591103
pseudo words 0.001584743
input data 0.001578002
single cluster 0.001569256
rich words 0.001557214
data our 0.001549052
cluster labeling 0.001545711
real data 0.0015403209999999999
minister cluster 0.001531525
noisy data 0.001503807
sense distinction 0.001493811
sense assignment 0.001489148
predominant sense 0.0014877570000000001
lexical features 0.001480004
order context 0.0014516730000000001
clustering labeling 0.001443431
few features 0.001431114
same name 0.0014091770000000002
agglomerative clustering 0.001385322
glomerative clustering 0.001382657
order vector 0.001364877
text vector 0.001358633
feature set 0.001347152
vector space 0.001342751
egorical features 0.001320888
words 0.00128494
semantic classes 0.001280162
context representation 0.00125493
cluster 0.00123114
sense 0.00122824
feature selection 0.001214291
semantic class 0.00120171
complete context 0.001198885
space model 0.001198386
discrimination clusters 0.001187037
high number 0.001170487
contingency matrix 0.001154899
assignment matrix 0.00115023
feature identification 0.00114108
different peo 0.001137411
clustering 0.00112886
entire context 0.001128664
english text 0.001126807
context vec 0.001120252
optimal number 0.001113458
exact number 0.001110846
results table 0.001104725
ebarak clusters 0.0010998079999999999
original number 0.001098427
first order 0.0010908760000000002
same distribution 0.001084001
singular value 0.00107703
name type 0.0010738499999999999
features 0.00106542
value decomposition 0.001064204
total number 0.001062194
name discrimination 0.0010586670000000001
ambiguous name 0.001039309
vector form 0.001032081
eraged vector 0.001032081
particular name 0.001030785
number spec 0.001029782
target name 0.001029624
single name 0.001018906
appropriate name 0.001014022
second order 0.001004691
original name 0.001003797
same person 9.94764E-4
experimental results 9.90286E-4
same referent 9.83511E-4
test contexts 9.82184E-4
order representation 9.77599E-4
baseline measure 9.72058E-4
general english 9.71265E-4
majority name 9.6641E-4
name label 9.505010000000001E-4
labeling results 9.490869999999999E-4
algorithm 9.42641E-4
