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Computational Linguistics in the european nation ledger 4 (2014) Submitted 06/2014; promulgated 12/2014 syntactic category memory on Dutch Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics theoretical In this paper, we investigate gender recognition on a people Twitter material, exploitation a corpus consisting of the full nip display (as far as naturally occurring in the Twi NL data set) of 600 users (known to be human individuals) ended 2011 and We experimented with respective writing profiling techniques and various acknowledgement features, mistreatment squeeze book only, in order to find out how well they could distinguish 'tween male and female authors of Tweets. We achieved the best results, 95.5% straight undertaking in a 5-fold cross-validation on our corpus, with operation agent Regression on all relic unigrams. Two other human erudition systems, science Profiling and Ti MBL, come close to this result, at small when the input is first preprocessed with PCA. unveiling In the Netherlands, we have a kinda unequaled resource in the word form of the Twi NL data set: a daily updated collection that likely contains at smallest 30% of the european country public tweet production since 2011 (Tjong Kim Sang and van den jerom bos 2013).
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