word vector 0.00368915
semantic word 0.00314425
word vectors 0.002950127
other word 0.002777446
word representations 0.002723235
neural word 0.002596881
word representation 0.002565915
word similarity 0.002559076
single word 0.002521758
word types 0.002486825
word pairs 0.002417597
word matrices 0.002415057
word pair 0.002413383
word level 0.002410928
vector models 0.002408946
various word 0.002351411
compositional model 0.002330567
initial word 0.002329437
svm word 0.002328081
word rep 0.002317068
word sequences 0.00231456
new model 0.002306155
consecutive word 0.002300844
space model 0.00225113
analysis model 0.002250879
model analysis 0.002250879
vector representations 0.002239145
network model 0.00217773
categorical model 0.002155409
model parameters 0.002143646
above model 0.002121315
unsupervised model 0.002117393
autoencoder model 0.002105259
compositional vector 0.002101557
vector representation 0.002081825
model com 0.002080882
only model 0.002069265
mvr model 0.002064083
whole model 0.002053354
powerful model 0.002052904
vector size 0.002049601
encoding model 0.002044878
cursion model 0.002044878
vector space 0.00202212
simple vector 0.001954467
parent vector 0.001929619
top vector 0.001910971
vector multiplication 0.001881394
function words 0.001880096
continuous vector 0.0018748670000000001
mantic vector 0.001864377
vector averaging 0.001858866
ple vector 0.00184416
movie sentiment 0.0018393139999999999
computing vector 0.001838759
vector spaces 0.0018377390000000001
vector sizes 0.00183577
vector repre 0.001833739
model 0.00183154
vector concatenation 0.00181549
tributed vector 0.00181549
such vectors 0.001710221
other words 0.0016826319999999999
semantic information 0.00167174
semantic composition 0.0016560199999999998
sentiment distribution 0.001609396
vector 0.00160253
sentiment analysis 0.001578879
previous sentiment 0.001568523
semantic similarity 0.001530086
context words 0.001507485
sentence sentiment 0.001503917
same function 0.0015005880000000002
composition function 0.00148668
sentiment distributions 0.001479705
sentiment prediction 0.001465592
sentiment experiments 0.001437545
single words 0.001426944
sentiment detection 0.001424706
semantic relationships 0.001424182
algorithm sentiment 0.001424023
composition models 0.001404806
compositional function 0.0013873169999999999
sentiment distribu 0.001383166
training data 0.001377774
several models 0.001343046
such features 0.0013421000000000002
semantic vec 0.001341722
multiple words 0.001331843
training error 0.0013258319999999999
semantic compositionality 0.001321916
phrase vectors 0.001321866
error function 0.001318141
training dataset 0.001314597
matrix representations 0.001311633
semantic com 0.001306972
semantic relations 0.001306675
semantic resources 0.0013064919999999998
compositional models 0.0013054429999999999
similar vectors 0.001280886
