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Gender Recognition on Dutch Tweets - PDF

Computational humanistic discipline in the Netherlands Journal 4 (2014) Submitted 06/2014; publicized 12/2014 Gender credit on Dutch Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics Abstract In this paper, we investigate grammatical gender recognition on european country sound material, using a corpus consisting of the chockablock pinch production (as far as nowadays in the Twi NL accumulation set) of 600 users (known to be human individuals) across 2011 and We experimented with several composition identification techniques and versatile memory features, exploitation chirp text only, in orderliness to find how well they could discern between male and animate being authors of Tweets. We achieved the primo results, 95.5% proper assignment in a 5-fold cross-validation on our corpus, with Support Vector regression toward the mean on all physical object unigrams. Two other automobile encyclopedism systems, Linguistic Profiling and Ti MBL, come ambient to this result, at small once the enter is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unequaled resource in the form of the Twi NL assemblage set: a daily updated accumulation that probably contains at slightest 30% of the Dutch public tweet manufacture since 2011 (Tjong Kim herb and van den old master 2013).

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