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For those techniques where hyperparameters need to be selected, we used a leave-one-out strategy on the test material. All users, obviously, should be individuals, and for each the gender should be clear.

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De var alene om det. Recognition accuracy as a function of the number of principal components provided to the systems, using token bigrams. Trigrams Three adjacent tokens. However, our starting point will always be SVR with token unigrams, this being the best performing combination.

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Original 5-gram About K features. Normalized 3-gram About 36K features.

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In this case, it would seem that the systems are thrown off by the political texts. Apparently, in our sample, politics is a male thing. In the example tweet, we find e.

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One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study Goswami et al. 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.

Bigrams Two adjacent tokens.

In fact, for all the tokens n-grams, it would seem that the further one goes away from the unigrams, the worse the accuracy gets. The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions.

TiMBL peaks a bit later at with 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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We aimed for users. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. For each setting and author, the systems report both a selected class and a floating point score, which can be used as a confidence score.

The dotted line represents exactly opposite scores for the two genders. SVR now already reaches its peak En redningshelikopter fra Aalborg ankom en halv time senere.