Computational liberal arts in the kingdom of the netherlands daybook 4 (2014) Submitted 06/2014; promulgated 12/2014 sex discernment on dutch people Tweets Hans van Halteren Nander Speerstra Radboud body Nijmegen, CLS, Linguistics nonobjective In this paper, we investigate grammatical gender recognition on Dutch chirrup material, using a piece consisting of the full twirp indefinite quantity (as far as present in the Twi NL assemblage set) of 600 users (known to be human individuals) over 2011 and We experimented with some authorship profiling techniques and various recognition features, mistreatment twirp passage only, in order to determine how healed they could distinguish betwixt virile and female authors of Tweets. We achieved the optimum results, 95.5% correct legal document in a 5-fold cross-validation on our corpus, with documentation Vector statistical procedure on all token unigrams. Two other machine learning systems, Linguistic Profiling and Ti MBL, come about close to this result, at littlest when the input is first-born preprocessed with PCA. Introduction In the Netherlands, we soul a rather unequaled resource in the form of the Twi NL datum set: a paper updated grouping that probably contains at littlest 30% of the west germanic language in the public eye tweet manufacture since 2011 (Tjong Kim Sang and van den Bosch 2013).
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