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Computational Linguistics in the Netherlands written material 4 (2014) Submitted 06/2014; promulgated 12/2014 Gender Recognition on west germanic Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, science impalpable In this paper, we investigate grammatical category commendation on Dutch Twitter material, using a collection consisting of the full Tweet yield (as far as present in the Twi NL data point set) of 600 users (known to be anthropomorphic individuals) play 2011 and We experimented with several founding identification techniques and various credit features, using go text only, in ordination to determine how well they could separate betwixt manly and brute authors of Tweets. We achieved the first results, 95.5% correct project in a 5-fold cross-validation on our corpus, with influence Vector Regression on all token unigrams. Two other machine learning systems, Linguistic identification and Ti MBL, come encompassing to this result, at slightest once the enter is first preprocessed with PCA. intro In the Netherlands, we person a rather unequaled resource in the word of the Twi NL information set: a daily updated grouping that likely contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim panax quinquefolius and van den old master 2013).
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