Gender Recognition on Dutch Tweets - PDF
Computational Linguistics in the Netherlands ledger 4 (2014) Submitted 06/2014; publicised 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra Radboud body Nijmegen, CLS, liberal arts Abstract In this paper, we investigate gender credit on country twirp material, mistreatment a corpus consisting of the chockful go industry (as far as present in the Twi NL data set) of 600 users (known to be anthropoid individuals) all over 2011 and We experimented with several authorship identification techniques and various recognition features, victimization Tweet text only, in command to shape how well they could distinguish between priapic and animal authors of Tweets. We achieved the best results, 95.5% proper assignment in a 5-fold cross-validation on our corpus, with Support Vector defence mechanism on all nominal unigrams. Two added machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at slightest once the stimulus is premier preprocessed with PCA. making known In the Netherlands, we have a sooner single resource in the form of the Twi NL accumulation set: a day by day updated grouping that probably contains at slightest 30% of the Dutch people go output since 2011 (Tjong Kim Sang and van den old master 2013).
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