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14-Apr-2016 20:38

However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata.

In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields.

And, obviously, it is unknown to which degree the information that is present is true.

Two other machine learning systems, Linguistic Profiling and Ti MBL, come close to this result, at least when the input is first preprocessed with PCA. Introduction In the Netherlands, we have a rather unique resource in the form of the Twi NL data set: a daily updated collection that probably contains at least 30% of the Dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013).

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

For gender, the system checks the profile for about 150 common male and 150 common female first names, as well as for gender related words, such as father, mother, wife and husband.

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

When using all user tweets, they reached an accuracy of 88.0%.We also varied the recognition features provided to the techniques, using both character and token n-grams.



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