word sense 0.00432038
word sentiment 0.0038128599999999995
sentiment word 0.0038128599999999995
word senses 0.002996986
context word 0.002874395
target word 0.0026881319999999998
word representations 0.002638358
sense disambiguation 0.002594277
word instance 0.002541181
word embeddings 0.0025055919999999996
art word 0.0024980339999999997
word embedding 0.0024976399999999998
alternative sense 0.002342259
adverbial sense 0.0023401240000000003
each sense 0.0023191830000000003
sense numbers 0.002316706
majority sense 0.002315962
cobuild sense 0.002312893
sense inventory 0.002305068
linear model 0.002298744
sense distinc 0.002293542
sense ambi 0.002293542
cesl sense 0.002293542
language model 0.002284585
model parameters 0.0021813699999999998
negative sentiment 0.002138639
same sentiment 0.0021360619999999998
gaussian model 0.0021230579999999997
single model 0.002118216
sentiment disambiguation 0.002086757
fication model 0.002069971
vlbl model 0.0020681049999999998
sense 0.00205855
sentiment analysis 0.002029603
accuracy sentiment 0.002004253
sentiment lexicon 0.0018907999999999998
different feature 0.001875219
sentiment contexts 0.0018669169999999998
sentiment lexicons 0.0018507349999999998
available sentiment 0.0018363449999999999
model 0.00183265
sentiment annotation 0.001828202
entire sentiment 0.001813107
sentiment classifica 0.0018088369999999998
accurate sentiment 0.001792691
sentiment lexi 0.001786928
sentiment indicators 0.001786177
sentiment lex 0.001786177
training set 0.001590001
individual words 0.001588645
feature set 0.0015823999999999999
other feature 0.001579595
similarity function 0.001562865
frequent words 0.001555154
sentiment 0.00155103
same feature 0.001536637
different polarity 0.001522949
other wsd 0.001464072
data set 0.001448725
context vector 0.001385236
corpus data 0.001367093
disambiguation data 0.0013536569999999999
learning features 0.001313178
words 0.00129034
estimation training 0.001256096
feature sets 0.00124611
feature type 0.00124535
contextual features 0.00124382
other models 0.001237755
feature types 0.001218711
ing features 0.001217622
context distribution 0.001203766
feature combinations 0.001203749
training time 0.001203673
training proce 0.001199335
different types 0.00119072
wsd approaches 0.001189745
feature combi 0.001187832
such features 0.001178418
different uses 0.001162553
different options 0.001159878
hard work 0.001157956
review data 0.001152536
vector representations 0.001149199
polarity classification 0.001142331
data structure 0.001113641
test set 0.0011090409999999998
related senses 0.0010995080000000001
contextual polarity 0.001096109
other work 0.001089344
semantic similarities 0.001083933
labeled data 0.001079848
new features 0.001077924
weight vector 0.00107464
semantic distinctions 0.001072621
classification results 0.001071661
novel features 0.001066375
accuracy polarity 0.0010525579999999999
ent senses 0.001042164
hard question 0.001041058
