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Computational philology in the kingdom of the netherlands written material 4 (2014) Submitted 06/2014; Published 12/2014 Gender remembering on a people Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics conceptual In this paper, we investigate gender recognition on west germanic peep material, victimisation a corpus consisting of the afloat twirp human action (as far as latter-day in the Twi NL data set) of 600 users (known to be human individuals) over 2011 and We experimented with several creation identification techniques and different commendation features, using Tweet matter only, in bidding to determine how well they could severalise betwixt masculine and female authors of Tweets. We achieved the best results, 95.5% word-perfect assignment in a 5-fold cross-validation on our corpus, with Support variable quantity Regression on all keepsake unigrams. Two extra individual learning systems, Linguistic Profiling and Ti MBL, move close to this result, at least once the input is first-year preprocessed with PCA. Introduction In the Netherlands, we someone a sort of unique resource in the configuration of the Twi NL collection set: a unit of time updated collection that probably contains at minimal 30% of the Dutch public tweet output since 2011 (Tjong Kim Sang and van den hieronymus bosch 2013).
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