gender classification 0.002746061
different gender 0.002652888
gender classifier 0.0026218960000000003
gender information 0.002420932
other features 0.0023881379999999997
gender differences 0.002348072
gender linguistic 0.002289615
single gender 0.002253893
certain gender 0.002226507
gender categories 0.002218734
gender category 0.002205018
gender pairs 0.002189938
appropriate gender 0.002178483
gender identity 0.0021617480000000002
gender detection 0.002161307
gender cate 0.002161042
opposite gender 0.0021598520000000003
gender distri 0.00215939
discrete gender 0.002157356
gender lin 0.002157356
positive features 0.001995602
gender 0.00197239
discriminative features 0.0018976989999999999
ful features 0.001894813
ple features 0.001893353
characteristic features 0.001892843
acoustic features 0.0018918539999999999
useful features 0.001890828
irrelevant features 0.001881109
male feature 0.001781343
features 0.00169401
feature selection 0.001649939
language models 0.0016013709999999999
language model 0.001585992
other words 0.001562722
individual feature 0.0014392519999999998
single language 0.001431733
classification accuracy 0.001428271
different data 0.001416369
ful feature 0.001397443
feature weighting 0.0013951369999999999
language processing 0.0013900289999999999
natural language 0.001380582
dependent language 0.00135631
language production 0.001336782
following words 0.001228896
feature 0.00119664
other speaker 0.001191281
text classification 0.0011898429999999999
classification performance 0.0011657759999999999
language 0.00115023
topic words 0.001146413
word use 0.00113272
first classifier 0.001124835
testing words 0.00110764
different text 0.0010966700000000001
classification method 0.001087034
word counts 0.001083168
high classification 0.0010727549999999999
discriminative words 0.001072283
characteristic words 0.001067427
other side 0.001056778
topic classification 0.00105149
training data 0.001042779
der classification 0.001030612
different methods 0.001021994
word dude 0.001014128
lexical differences 0.001011424
word fragments 0.0010000719999999999
classification accuracies 9.79961E-4
classification algorithms 9.776799999999999E-4
testing data 9.749170000000001E-4
other conversation 9.54855E-4
lexical use 9.538000000000001E-4
data preparation 9.28253E-4
topic classifier 9.27325E-4
data collections 9.26952E-4
focused data 9.21487E-4
different distributions 9.17985E-4
second classifier 9.109879999999999E-4
other hand 8.841680000000001E-4
different properties 8.740709999999999E-4
speaker differences 8.72835E-4
words 8.68594E-4
different modes 8.661389999999999E-4
perfect accuracy 8.625379999999999E-4
test set 8.57909E-4
lexical tokens 8.55051E-4
sification accuracy 8.53723E-4
lexical usage 8.52395E-4
same table 8.43978E-4
accuracy drops 8.40101E-4
lexical richness 8.2117E-4
female dude 8.16293E-4
analysis techniques 8.11193E-4
selection methods 7.94795E-4
current speaker 7.8545E-4
male indicators 7.84001E-4
possible terms 7.82125E-4
classification 7.73671E-4
