De westelijke zuidzijde wordt vol getroffen. De sneeuwkaart van vandaag toont mooi de gebieden waar er veel sneeuw verwacht wordt. Lokaal vandaag al tot 40-50 cm bij een gunstige Stau. Zonnig weer in Arc1800. @liveupdates, regengeulen in Flaine, er ligt wel nog ruim genoeg sneeuw. Heel ander weer aan de noordkant en de rest van de zuidzijde waar het droog blijft. Wel moet de hele dag rekening gehouden worden met hoge/middelhoge wolkenvelden. Föhn opklaringen zijn mogelijk welke zich vooral vormen verder van de Alpenhoofdkam.

dan vochtige lucht tegen de bergen, wordt gedwongen te stijgen, condenseert en levert veel neerslag. Hoeveelheden tot middernacht liggen in de marge van 30-50 cm in de gunstige Stau-gebieden. Deze kaart van Wetteronline toont de neerslaggebieden voor vandaag.

Fantastische weer en fantastische sneeuw. Wat wil je nog meer? De wind draaide gisteren naar het zuiden. Aan de zuidkant domineerde bewolking, aan de noordkant scheen de zon. Vandaag steeds meer sneeuw aan (westelijke) zuidzijde. Zoals gezegd in de inleiding bevindt een hoogtelaag zich boven het Iberisch schiereiland. Aan de oostkant hiervan waait vochtige middellandse zeelucht vanaf het zuiden naar de Alpen. Een hoogtelaag ligt boven het Iberisch Schiereiland en zorgt voor een Südstau in de Alpen. Vandaag komt de südstau helemaal op gang aan de westelijke zuidzijde. Reeds vanaf de ochtend valt daar matige sneeuwval welke de hele dag zal aanhouden. Sneeuwgrens schommelt gemiddeld rond meter maar kan lokaal nog iets lager uitvallen tijdens intense neerslag.

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Zondag 08:32 naar de reacties, algemeen, een hoogtelaag heeft zich afgesnoerd en bevindt zich boven het Iberisch schiereiland. Rond ooit een cycloon waait de wind tegen de wijzers in op het noordelijk halfrond waardoor vochtige wind van over de middellandse zee wordt gestuwd tegen de zuidkant van de Alpen. Dit levert tot en met dinsdag grote hoeveelheden neerslag op die boven gemiddeld 1400 meter als sneeuw vallen. Terwijl de noordkant vorige week getroffen werd door hevige neerslag is nu de zuidkant aan de beurt. De precieze hoeveelheden zijn altijd moeilijk te schatten met dergelijke stuw maar 1-2 meter lijkt zeker mogelijk! Prachtige winterse plaat uit Mittelberg. Door de föhn werd het er gisteren wel. @Twitter Hans ter braak.

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2 Fink. (2012) used svmlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets. Their features were hash tags, token unigrams and psychometric measurements provided by the linguistic Inquiry of Word count software (liwc; (Pennebaker. Although liwc appears a very interesting addition, it hardly adds anything to the classification. With only token unigrams, the recognition accuracy was.5, while using all features together increased this only slightly.6. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English. They used lexical features, and present a very good breakdown of various word types. When using all user tweets, they reached an accuracy.0.

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The creators themselves used it for various classification tasks, including gender recognition (Koppel. They report an overall accuracy.1. Slightly more information seems to heracles be coming from content (75.1 accuracy) than from style (72.0 accuracy). However, even style appears to mirror content. We see the women focusing on personal matters, leading to important content words like love and boyfriend, and important style words like i and other personal pronouns. 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. 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.

2009) managed to increase the gender recognition quality.2, using sentence length, 35 non-dictionary words, and 52 slang words. 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. Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use. With lexical N-grams, they reached an accuracy.7, which the combination with the sociolinguistic features increased.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. Their highest score when using just text features was.5, testing on all the tweets by each author (with a train set.3 million tweets and a test set of about 418,000 tweets).

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In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section. A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe koppel. In (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 omega function words and parts of speech. Later, in 2004, the group collected a blog Authorship Corpus (BAC; (Schler. 2006 containing about 700,000 posts to m (in total about 140 million words) by almost 20,000 bloggers. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. This corpus has been used extensively since.

super stille fohn

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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. C 2014 van Halteren and Speerstra. Gender Recognition Gender recognition is a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades(for an overview, see. (Juola 2008) and (Koppel. Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. (2012) show that authorship recognition is also possible pegboard (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies). Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling,. The identification of author traits like gender, age and geographical background.

In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. For fitting our experiment, we selected 600 authors for whom we were able to determine with a high degree of certainty a) that they were human individuals and b) what gender they were. We then experimented with several author profiling techniques, namely support Vector Regression (as provided by libsvm; (Chang and Lin 2011 linguistic Profiling (LP; (van Halteren 2004 and timbl (Daelemans. 2004 with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901 (Hotelling 1933). We also varied the recognition features provided to the techniques, using both character and token n-grams. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets. In the following sections, we first present some previous work on gender recognition (Section 2). Then we describe our experimental data and the evaluation method (Section 3 after which we proceed to describe the various author profiling strategies that we investigated (Section 4).

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1 Computational Linguistics in the netherlands journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra radboud University nijmegen, cls, linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting. We achieved the best results,.5 correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams. Two other machine learning systems, linguistic Profiling and timbl, 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 opsteken in the form of the Twinl 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). 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. 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.

Super stille fohn
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