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They used lexical features, and present a very good breakdown of various word types.

However, even style appears to mirror content. And TiMBL is currently underperforming, but might be a challenger to SVR when provided with a better hyperparameter selection mechanism. And LP just mirrors its behaviour with unigrams.

Figure 5 shows all token unigrams. The best recognizable female, authoris not as focused as her male counterpart.

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For the other feature types, we see some variation, but most scores are found near the top of the lists. Again, we take the token unigrams as a starting point. In this case, it would seem that the systems are thrown off by the political texts.

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In addition, the recognition is of course also influenced by our particular selection of authors, as we will see shortly. 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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Furthermore, LP appears to suffer some kind of mathematical breakdown for higher numbers of components. The tokenizer counts on clear markers for these, e.

This corpus has been used extensively since. Feature type Unigram Bigram Trigram Skipgram Char 5-gram Top Function 14 get the impression that Dutch is not his native language, which is supported by his name. It then chose the class for which the final score is highest.

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We will only look at the final scores for each combination, and forgo the extra detail of any underlying separate male and female model scores which we have for SVR and LP; see above. These statistics are derived from the users profile information by way of some heuristics.

However, looking at SVR is not an option here. 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. Apparently, in our sample, politics is a male thing.

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A group which is very active in studying gender recognition among other traits on the basis of text is that around Moshe Koppel. When using all user tweets, they reached an accuracy of For the bigrams Figure 2we see much the same picture, although there are differences in the details.

When adding more information sources, such as profile fields, they reach an accuracy of As scaling is not possible when there are columns with constant values, such columns were removed first. We aimed for users. We expect that the performance with TiMBL can be improved greatly with the development of a better hyperparameter selection mechanism.

However, all systems are in principle able to reach the same quality i. The men, on the other hand, seem to be more interested in computers, leading to important content words like software Christmas gift ideas for newly dating game, and correspondingly more determiners and prepositions.

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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. Normalized 1-gram About features. For whom we already know that they are an individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed.

Figure 4 shows that the male population contains some more extreme exponents than the female population.

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