semantic features 0.00304594
semantic feature 0.0025579
semantic similarity 0.0023085840000000002
global semantic 0.002110545
semantic meaning 0.002082942
semantic contexts 0.002057108
semantic fea 0.0020488060000000002
semantic con 0.0020431670000000002
semantic links 0.0020378230000000002
semantic annotation 0.002032177
semantic feafure 0.002001325
rank semantic 0.001994276
features social 0.0019631699999999998
social features 0.0019631699999999998
semantic 0.00179406
lexical features 0.001662313
same data 0.00157996
various features 0.001573644
global features 0.001568365
surface features 0.001536724
tic features 0.001523061
features surf 0.001505577
novel features 0.0015048739999999998
correlation features 0.001486543
mantic features 0.0014864689999999998
features simti 0.001461465
features censored 0.0014536529999999998
data set 0.001429226
social social 0.00142258
information network 0.0014121440000000002
social user 0.001348064
similar information 0.001331502
temporal information 0.001327079
information extraction 0.001301849
ing information 0.001295987
data example 0.001287181
same set 0.001264832
chinese word 0.001259479
features 0.00125188
data sets 0.001251342
same relation 0.0012491540000000001
information due 0.00124342
related information 0.001229424
information morph 0.0012236830000000001
social network 0.0012201669999999999
entity entity 0.001213854
important information 0.001209143
genre information 0.001181378
different object 0.001167984
information science 0.001166301
comparable data 0.001165136
source information 0.001163035
feature extraction 0.001162422
weibo data 0.001147646
information morphs 0.0011461330000000001
phenomenon information 0.001145385
information networks 0.001144568
feature sets 0.0011430049999999999
tive information 0.001139478
different types 0.0011374150000000001
relation type 0.001136781
data source 0.001131945
information loss 0.00112979
information censorship 0.001127785
data acquisition 0.001122447
information net 0.001122364
data figure 0.0011171430000000001
internet information 0.001113261
neous information 0.001105649
erogeneous information 0.0011037780000000001
markov model 0.001101887
data uncensored 0.001098635
uncensored data 0.001098635
same time 0.001098031
communication data 0.001093246
regression model 0.0010825
social distance 0.001078864
censored data 0.00107395
such networks 0.001061803
single feature 0.00105267
first set 0.0010419890000000001
target learning 0.001034777
other types 0.0010342670000000002
target candidate 0.001031023
chinese news 0.001025433
such discrepancies 0.00102162
social interactions 0.001020582
same length 0.001014719
morph query 0.0010117070000000001
similarity search 0.0010080010000000001
different neighbor 9.99652E-4
object type 9.99189E-4
system performance 9.98392E-4
entity event 9.88109E-4
new set 9.85681E-4
social media 9.85412E-4
similarity score 9.76327E-4
social events 9.69298E-4
social fea 9.66036E-4
certain type 9.61217E-4
