W12-0504 ) shows a confusion matrix for gender identification . Out of 1890 frames in which
W12-0504 occasions . On the other hand gender identification is not always an easy task even
W10-0305 matching against asai pattern , gender identification , word repetition and adding
W14-0906 in ( Schler et al. , 2006 ) for gender identification , applied to the identification
H01-1059 speaker ids were directly used for gender identification since in previous experiments
D13-1114 gender does not automatically make gender identification in French tweets a trivial task
D13-1187 limited amount of work focusing on gender identification has looked at differences in
D11-1120 well such models carry over to gender identification in other informal online genres
W11-1709 primarily been done for automatic gender identification ( Cheng et al. , 2009 ; Corney
D15-1256 are unavailable . 3.1.2 Age and gender identification To estimate the distribution
P11-1077 in writing style depending on gender identification ( Herring and Paolillo , 2006
D13-1187 of sentiment have been used in gender identification , to the best of our knowledge
W12-0604 50s ) and up to 91 % accurate on gender identification . This is quite different than
W15-5409 of shared tasks : i ) Age and gender identification at the Author Profiling task
P13-2150 like authorship attribution , gender identification , and native-language identification
W14-0902 Hota et al. ( 2006 ) on automatic gender identification in Shakespeare 's texts , as
D15-1240 for training . 3 Experiments 3.1 Gender Identification Data was collected from the OkCupid
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