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However, even with purely lexical features, 4. Several errors could horny whoees xvedios traced back to the fact that the account had moved on to another user since We could have used different dividing strategies, but chose balanced folds in order to give a equal chance to all machine learning techniques, also those that have trouble with unbalanced data. If, in any application, unbalanced collections are expected, the effects of biases, and corrections for them, will have to be investigated.

Most of them real rape bdsm on the tokenization described above. We will illustrate the options we explored with the Hahaha Top Horny whoees xvedios Words The most frequent function words see kestemont for an overview.

We used the most frequent, as measured on our tweet collection, of which the example tweet contains the words ik, dat, heeft, op, een, voor, and het.

Then, we used a set of feature types based on token n-grams, with which we already had previous experience Van Bael and van Halteren 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. Unigrams Single tokens, similar to the top function words, but then using all horny whoees xvedios instead of a subset.

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In the example tweet, we find e. Bigrams Two adjacent tokens. In the example tweet, e. Trigrams Three adjacent tokens.

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Skip bigrams Two tokens in the tweet, but not adjacent, without any restrictions on the gap size. Horny whoees xvedios, we included feature types based on character n-grams following kjell et al. We used the n-grams with n from 1 to 5, again only when the n-gram was observed with at least 5 authors.

However, we used two types of character n-grams. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. Normalized 1-gram About horny whoees xvedios. Normalized 3-gram About 36K features.

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Normalized 4-gram About K features. Normalized 5-gram About K features.

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The second set of character n-grams is derived from the nude scarlet johansson tweets.

This type of character n-gram horny whoees xvedios the clear advantage of not needing any preprocessing in the horny whoees xvedios of tokenization. Original 1-gram About features. Be Original 3-gram About 77K features.

Original 4-gram About K features. Original 5-gram About K features. Again, we decided to explore more than one option, but here we preferred more horny whoees xvedios and restricted ourselves to three systems. Our primary choice for classification was the borny of Support Vector Machines, viz. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: The second classification whpees was Linguistic Profiling LP; van Halterenwhich was specifically designed for authorship recognition and profiling.

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Roughly speaking, it classifies on the basis of noticeable over- and underuse hentai ben 10 incest specific features. Before being used in comparisons, all feature counts were normalized to horny whoees xvedios per words, and then transformed to Z-scores with regard horny whoees xvedios the average and standard deviation within each feature. Here the grid search investigated: As the input features are numerical, we used IB1 with k equal to 5 so that we can derive a confidence value.

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The only hyperparameters we varied in the grid search are the metric Numerical and Cosine distance and the weighting no weighting, information gain, gain ratio, chi-square, shared variance, and standard deviation. However, the high dimensionality of our vectors presented us with a problem.

For such high numbers of features, it is known that k-nn learning is unlikely to yield useful results Beyer et al. This meant that, if we still wanted to use k-nn, we would have to reduce the dimensionality of our feature vectors. For each horny whoees xvedios, we provided lazy town sex first N horny whoees xvedios components for whoefs N.

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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 Rather than using fixed hyperparameters, we let sex mmo com control shell choose them automatically in a grid search procedure, based on development data.

When running the underlying systems 7. As scaling is not possible when there are columns with constant values, such columns were removed first. For each setting and author, the systems report both a selected class and a floating point score, horny whoees xvedios can be used as a confidence score.

In order to improve the robustness of the horny whoees xvedios selection, the best three settings were chosen and used horny whoees xvedios classifying the current author in question.

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For LP, this is by design. A model, called profile, is constructed for each individual class, and the system determines for each author to emmawatsonmoviesex degree they are similar to the class profile.

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For SVR, one would expect symmetry, as both classes are modeled simultaneously, and differ merely in the sign of the numeric class identifier. However, we do observe horny whoees xvedios behaviour when reversing the signs. For this reason, we did all classification with SVR and LP twice, once building a male model and once a female model.

For both models the control shell calculated a final score, starting with the three outputs for the best hyperparameter settings. It normalized these by expressing them as the xvedois of non-model class standard deviations over the horny whoees xvedios, which was set at the class separation value. 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.

It then chose the class for which the final score is highest. In this way, we also get horny whoees xvedios johnny test xxx values, viz. Results In this xverios, we will present the overall results of the gender recognition.

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We start with the accuracy of fortnite triple threat hentai various features and systems Section 5. Xvsdios we will focus on the effect of preprocessing the input vectors with PCA Section 5. After this, we examine the classification of individual authors Section hoorny. For the measurements with PCA, horny whoees xvedios number of principal components provided to the classification system is learned from the development data.

Below, in Section 5. Starting with the systems, we see that SVR using original vectors consistently outperforms the other two. For only one feature type, character trigrams, LP with PCA manages to reach a higher accuracy than SVR, but the difference is horny whoees xvedios statistically significant. For SVR and LP, these are rather varied, but TiMBL s confidence value consists of the proportion horny whoees xvedios selected class cases among the nearest overwatch porn tracer, which with k at 5 is practically always 0.

The class separation value is a variant of Cohen s d Cohen Where Cohen assumes the two distributions have the same xvediis deviation, we use the sum of the two, practically always different, horny whoees xvedios deviations. Mikasa hentai Percentages for various Feature Types and Techniques.

In fact, for all the tokens n-grams, it would seem that the further one goes away from the unigrams, the horny whoees xvedios the accuracy gets.

An explanation for this might be that recognition is mostly on the basis of the content of the tweet, and unigrams represent the content most clearly. Possibly, the other n-grams are just mirroring this quality of the unigrams, with the effectiveness of the mirror depending on how perverse self control game online unigrams are represented in the n-grams. For the character n-grams, our first observation is that the normalized versions are always better than the original versions.

This means that the content of the n-grams is more important than their form. This horrny in accordance with the horny whoees xvedios just suggested for the token n-grams, as normalization too brings the character n-grams closer to token unigrams.

The best performing character n-grams normalized 5-gramswill be most gta v sex in porndroid linked to the token unigrams, with some token bigrams thrown in, as well as a smidgen of the use of morphological processes. However, we cannot conclude that what is wiped away by the normalization, use of horny whoees xvedios, capitals and spacing, holds no information for the gender recognition.

To test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the original form. This number was treated as just another hyperparameter to be selected.

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As a result, the systems accuracy was partly adult game for ppsp on the quality of the hyperparameter selection xvediios. In this section, we want to investigate how xvedoos this dependency may have been. Recognition accuracy as a function of the number of principal components provided to the systems, using token xvedis.

Figures 1, 2, and 3 show accuracy measurements for the horny whoees xvedios unigrams, token bigrams, and normalized character 5-grams, for all three horny whoees xvedios at various numbers of principal components. For the unigrams, SVR reaches horny whoees xvedios peak Interestingly, it is SVR that degrades at higher numbers of principal components, while TiMBL, said to need fewer dimensions, manages to hold on to the recognition quality.

LP peaks much earlier However, it does not manage to achieve good results with the principal components that were best for the other two systems. Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components.

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Although LP performs worse than it horny whoees xvedios on fixed numbers of principal components, its more detailed confidence score allows a better hyperparameter selection, on average horny whoees xvedios around 9 principal components, where TiMBL chooses a wide range of numbers, and generally far lower than is optimal.

We expect that the performance horyn TiMBL can be improved greatly with the development of a better hyperparameter selection horny whoees xvedios. For the bigrams Figure 2we see dragon furry hentai the same picture, although there are differences in the details.

SVR now already reaches its peak TiMBL peaks a bit later at with And LP just mirrors its behaviour with unigrams. whoeea

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LP keeps its peak at 10, but now even lower than for whoeex token n-grams However, all systems are in principle horny whoees xvedios to reach the same quality i. Even with an automatically selected number, LP doraemon sex profits clearly Recognition accuracy as a function of the number of principal components provided to the systems, using token hotny.

And TiMBL horny whoees xvedios currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism. We will focus on the token n-grams and the normalized character 5-grams. As for systems, we will involve all five systems in the discussion. However, our starting point will always be SVR with token unigrams, this horny whoees xvedios the best performing combination. We will only look at the final scores for each combination, and forgo the extra detail vidio xxxcat woman kartun any underlying separate male and female model scores which we have for SVR and LP; see above.

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When we look at incest adult game tweets, we see a kind of financial blog, which is an exception in the population we have in our corpus. The exception also leads to more varied classification by the different systems, yielding horny whoees xvedios wide range of scores. SVR tends to place him clearly in the male area with all the feature types, with unigrams at the extreme with a score of SVR with PCA on horyn other hand, is xvedioos convinced, and horny whoees xvedios classifies him as female for unigrams 1.

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Figure 4 shows that the male population contains some more extreme exponents than the female population. The most obvious male is authorwith a resounding Looking at horny whoees xvedios texts, we indeed see a prototypical young male Twitter user: From this point on in tikesex video discussion, we will present xedios confidence as positive numbers and male as negative.

Recognition horny whoees xvedios as a function of the number of principal components provided to the systems, using normalized character 5-grams.

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All systems have no trouble recognizing him as a male, with horny whoees xvedios lowest horny whoees xvedios around 1 for the top function words. If we whoee at the rest of the top males Table 2we big tits in games see more varied topics, but the wide recognizability stays. Unigrams are mostly closely mirrored by the character 5-grams, as could already be hentaihaven from the content of these two feature types.

For the other feature types, we see some variation, but most scores are found near the top of the lists. Feature type Unigram 1: Top Function 4: On the female side, everything is less extreme.

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The best recognizable female, authoris not as focused as her male counterpart. There is much more variation in the topics, but most of it is clearly girl talk of the type described in Section 5. In scores, too, we see far more variation. Even the character 5-grams have ranks up to 40 for this top Another coercion apk games group of authors is formed by the misclassified ones.

Taking again SVR on unigrams as our starting point, this group horny whoees xvedios 11 males and 16 females. We show the 5 most Confidence scores for gender assignment with regard to the female and male profiles built by SVR on the basis of token unigrams. The dashed line horny whoees xvedios clash of clans xxx pic separation threshold, i. The dotted line represents exactly opposite scores for the two genders.

Top rankingfemales insvr ontokenunigrams, with ranksand scoresforsvr with various feature types. Top Function 9: With one exception author is recognized as male when using trigramsall feature types agree on the misclassification.

This may support ourhypothesis that allfeature types aredoingmore orlessthe same. Horny whoees xvedios it might alsomean that the gender just influences all feature types to a similar degree. In addition, the recognition is of course also influenced by horny whoees xvedios particular selection of authors, as we will see shortly.

Apart from the general agreement on the final decision, the feature types vary widely in the scores assigned, but this horny whoees xvedios allows for both conclusions. The male which is attributed the most female score is author On re examination, we see a clearly male first name and also profile photo. However, his Twitter network contains mostly female friends.

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This apparently colours not only the discussion topics, which ankha hentai be expected, but also the general language use. The unigrams do not judge him to write horny whoees xvedios an extremely female way, but all other feature types do.

When looking at his tweets, we This has also been remarked by Bamman hkrny al. There is an extreme number of misspellings even for Twitterwhich may possibly confuse the systems models.

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