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This is in accordance with the hypothesis just suggested for the token n-grams, as normalization too brings the character n-grams closer to token unigrams. The unigrams do not judge him to write in an extremely female way, but all other feature types do.

The word haar may be the pronoun her, but just as well the noun hair, and in both cases it is actually more related to the Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components.

The class separation value is a variant of Cohen s d Cohen Experimental Data and Evaluation In this section, we first describe the corpus that we used in our experiments Section 3.

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A new version of this license is available. After this, we examine the classification of individual authors Section 5.

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Be Original 3-gram About 77K features. They report an overall accuracy of As the input features are numerical, we used IB1 with k equal to 5 so that we can derive a confidence value. The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well.

Normalized 5-gram About K features. Trigrams Three adjacent tokens. The first set is derived from the tokenizer output, and can be viewed as a kind of normalized character n-grams. In this section, we want to investigate how strong this dependency may have been.

To test that, we would have to experiment with a new feature types, modeling exactly the difference between the normalized and the original form.

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Original 1-gram About features. And TiMBL is currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism. Although we agree with Nguyen et al.

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Here the grid search investigated: The exception also leads to more varied classification by the different systems, yielding a wide range of scores.