different domain 0.004491022
domain model 0.004420002
domain adaptation 0.004289883
same domain 0.0042453890000000005
domain label 0.004149910000000001
domain information 0.004104291
domain difference 0.004084789
domain labels 0.004054729
empirical domain 0.004046091
shared domain 0.004039802
automatic domain 0.0040089
domain distance 0.004003795
domain partition 0.003986555
detailed domain 0.003958996
bad domain 0.00395386
easy domain 0.0039494180000000005
domain size 0.003933224
informative domain 0.003930386
domain partitioning 0.003921788
domain identities 0.003921353
domain assignments 0.003919416
tomatic domain 0.00391926
domain indicators 0.003919256
domain partitions 0.003918335
domain identity 0.003917554
domain dif 0.003916495
pirical domain 0.003916495
domain identi 0.003916495
sirable domain 0.003916495
truth domain 0.003916495
domain parti 0.003916495
domain par 0.003916495
domain 0.00368035
data matrix 0.0023493209999999997
training data 0.00233608
data set 0.002197397
data instance 0.0021512099999999998
ing data 0.002088155
data instances 0.002069478
analysis data 0.0020500279999999997
data pairs 0.002031086
book data 0.002010873
data groups 0.00198187
data sets 0.001976977
data points 0.001974835
prod data 0.001933943
meta data 0.00193349
data generation 0.001927645
data pairsd 0.001927645
feature matrix 0.001872451
feature distribution 0.001789223
different domains 0.0017496270000000001
learning methods 0.0016230189999999999
other domains 0.001579779
feature space 0.001543085
augmented feature 0.001470973
several domains 0.0014580230000000001
input features 0.001429174
amazon sentiment 0.001328647
other methods 0.001305404
small domains 0.001303142
sentiment analysis 0.0012793840000000002
svm classifier 0.001273719
metric learning 0.0012701939999999999
real sentiment 0.001258272
output domains 0.001253678
machine learning 0.001250131
feature 0.00121465
different distributions 0.001205145
model loss 0.001195519
ferent domains 0.001183795
ated domains 0.001175293
inal domains 0.001175293
prediction model 0.00117462
mdl algorithm 0.001173277
mdl methods 0.001155459
training set 0.0011504369999999998
different pairs 0.001150238
test method 0.001143593
same distribution 0.001139612
new approach 0.00112431
test results 0.0011104370000000001
classifier combination 0.001107231
different groups 0.0011010220000000001
clustering algorithm 0.001096646
mdl performance 0.001075639
features 0.00106524
different size 0.001063546
baseline performance 0.001049967
different thay 0.001047582
different initializations 0.001047582
amazon product 0.001040412
second mdl 0.001029644
product reviews 0.00102881
common product 0.001026725
other user 0.0010153200000000001
product review 0.001010085
clustering method 0.001006512
trained model 9.97629E-4
original training 9.93142E-4
