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En burka dækker hele kroppen og omfatter et lille netgardin til kvindens øjne. Anvendelsen af nikab er frem for alt udbredt i Saudiarabien og i landene omkring Den Persiske Bugt.

EPiger som er klækkelig klædt og mere tildækket, pirre ofte en mands nysgerrighed mere... Hvis ja - så har det modsatte effekt, end det er tiltænkt ifølge deres religion..?

A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work), reaching about 80% correct attributions using function words and parts of speech.

Later, in 2004, the group collected a Blog Authorship Corpus (BAC; (Schler et al.

Then follow the results (Section 5), and Section 6 concludes the paper. 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. the identification of author traits like gender, age and geographical background.

In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section.

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%.

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

The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.

With lexical N-grams, they reached an accuracy of 67.7%, which the combination with the sociolinguistic features increased to 72.33%. (2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (SVM), Naive Bayes and Balanced Winnow2.

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