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Instead, we will just look at the distribution of the various features over the female and male texts.

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. If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated.

Recognition accuracy as a function of the number of principal components provided to the systems, using token unigrams. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets.

These statistics are derived from the users profile information by way of some heuristics.

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Starting with the systems, we see that SVR using original vectors Goed dating profiel voorbeeld outperforms the other two. You are free to: They report an overall accuracy of However, looking at SVR is not an option here. Finally, we included feature types based on character n-grams following kjell et al.

Their highest score when using just text features was The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams.

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However, it does not manage to achieve good results with the principal components that were best for the other two systems. In effect, this N is a further hyperparameter, which we varied from 1 to the total number of components usuallyas there are authorsusing a stepsize of 1 from 1 to 10, and then slowly increasing the stepsize to a maximum of 20 when over Original 1-gram About features.

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But it might alsomean that the gender just influences all feature types to a similar degree. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. The control shell then weighted each score by multiplying it by the class separation value on the development data for the settings in question, and derived the final score by averaging.

Then we outline how we evaluated the various strategies Section 3. As scaling is not possible when there are columns with constant values, such columns were removed first. In addition, the recognition is of course also influenced by our particular selection of authors, as we will see shortly.

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This restriction brought the number of users down to aboutTop rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types. Normalized 4-gram About K features. For all feature types, we used only those features which were observed with at least 5 authors in our whole collection for skip bigrams 10 authors.

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For gender, the system checks the profile for about common male and common female first names, as well as for gender related words, such as father, mother, wife and husband. This means that the content of the n-grams is more important than their form.

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