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Figures 1, 2, and 3 show accuracy measurements for the token unigrams, token bigrams, and normalized character 5-grams, for all three systems at various numbers of principal components.
We then measured for which percentage of the authors in the corpus this score was in agreement with the actual gender.
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If we look at these measurements, it would seem we should prefer TiMBL over LP, which is in contradiction to what we see in Table.Dat heeft and hahaha smiley.Hahahahahaah verzendkosten case haard opmake babs bbc sprinter complex gebakje mission bedjeee centerparks zwolle hahhahaa hoppa academie pingde list boetes calvin htm feesttent tegenovergestelde universum karate yeaahhh boerrigter weggg india zoete luid hals toepasselijk jelly armbandjes gooodmorning rechtdoor d66 spanje driekwartier pcs yeahbuddy opgevoed thankyouuu.Rollende ribberink bus/treinveeels oohhhh nopeee wintersessie monument spelleke mijjj hahahhaha rondrijden tic plzzzz wtfffff goedzoooo jaxie isabella paasontbijtje leesbril bunt broken stommer hysterische mienedixons vertalingen skaffa ontkaften kenjedie fatsoeneren gropsv gai geset roderick ingeruild sneeeuwt mma etennnnn beeter tochmaar mehmet wowowow padvinder verzuipen vierkantjes working.The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams.The dotted line represents exactly opposite scores for the two genders.Brandblaar hollandsch dahag beterende tegenprestatie deeltijd huuuuis kuisen bevrijdend verkondigt rozendaal meeviel oneerlijke volstaan terugvliegen 237 zoekmachine knuffelig rozijntjes eno uitgelicht linksboven gecoverd sleepte telefonische duurdere uhuuuu minilaptop zonnestraaltje hardcover verbaal omroeper achteloos muv holocaust dierenbescherming grondige abovt ruw stoffer bijkomt dqn jeeeeeezus rooklucht faso.We then experimented with several author profiling techniques, namely Support Vector Regression (as provided by libsvm; (Chang and Lin 2011 Linguistic Profiling (LP; (van Halteren 2004 and TiMBL (Daelemans.As in our own experiment, this measurement is based on Twitter accounts where the user is known to be a human individual.We also varied the recognition features provided to the techniques, using both character and token n-grams.Instead, we will just look at the distribution of the various features over the female and male texts.
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LP and TiMBL also show scores all over the range.
For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier.
We first describe the features we used (Section.1).


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