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topic models 0.004043706
same topic 0.003807833
topic modeling 0.0037959440000000003
travel topic 0.00356318
ing topic 0.003561752
topic structure 0.003527056
topic mixture 0.003516824
markov topic 0.003505184
unsupervised topic 0.003502114
topic dis 0.0034889400000000003
food topic 0.0034874520000000003
environment topic 0.0034761980000000002
special topic 0.003468384
pets topic 0.003466853
topic assignments 0.00346187
topic mixtures 0.003456157
primary topic 0.0034526300000000004
health topic 0.003437467
unrelated topic 0.003437467
topic describ 0.003437467
topic 0.00324534
word distribution 0.0027847899999999997
different word 0.002202601
word distributions 0.001975991
such model 0.001885984
previous word 0.001856749
multinomial word 0.001818251
indian word 0.001811498
new model 0.0017824870000000001
probability distribution 0.001781609
word dis 0.00177165
word distribu 0.001769268
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word clusters 0.00175232
shared word 0.001740624
native word 0.001740238
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pore word 0.001720521
word polarity 0.001720521
word separators 0.001720521
dirichlet distribution 0.001703525
such topics 0.001698764
generative model 0.001673671
specific distribution 0.001639135
singapore distribution 0.0016120969999999998
related model 0.001589575
model evaluation 0.001579933
ccmix model 0.001569496
many topics 0.001556426
bayes model 0.0015542680000000001
indian distribution 0.0015401879999999998
mixture distribution 0.001528224
topical words 0.001527408
india distribution 0.00152174
function words 0.0014919669999999999
different models 0.001472917
shared distribution 0.0014693139999999998
bernoulli distribution 0.0014581009999999998
arbitrary distribution 0.001453308
beta distribution 0.001449838
certain topics 0.00144399
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related topics 0.0014023549999999999
words restaurant 0.00139307
own topics 0.001388673
top words 0.001366738
flag topics 0.001350581
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topics auto 0.001350581
model 0.00134482
lda models 0.001304839
common words 0.0012989149999999999
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stop words 0.001267963
distribution 0.00125674
shared words 0.0012475339999999998
background words 0.0012462299999999999
vironmental words 0.001227955
topics 0.0011576
blog data 0.0011490580000000001
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locals data 0.001098184
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tourists data 0.001081328
modeling text 0.001042626
unannotated data 0.001035474
words 0.00103496
different top 0.001006329
bilistic models 9.9052E-4
high probability 9.75484E-4
probability distributions 9.7281E-4
gle document 9.29671E-4
different countries 9.27517E-4
document topicality 9.26683E-4
same set 9.16919E-4
important language 9.07669E-4
different degree 9.0199E-4
other differences 9.01397E-4
language processing 8.99544E-4
