Da drogist hillegom

da drogist hillegom

'Anders neem je er eentje voor je vriendin. 'hi kim.' zei hij vrolijk toen ze haar zag. 'dan kan ik toch niet ademen, dat is benauwend schat.' ron baalde, maar ze had gelijk. 'Probeer je nou mij te neuken, of een ander jong ding?' zei ze spottend en kreunde. 'de betaling doet mijn man, én keer in de maand. 'ja pieter, ingerie afdeling.' zei ze grappend, omdat zij tot nu toe de klanten hier geholpen had. 'denk je dat het nachtlampje vanzelf uitgaat?' Shit, hij stond er niet eens bij stil. 'ik zei toch dat je het aankon.

#belgie #belgium #fruit #fruitcake #strawberries #banana #kiwi #grape #pineapple #pudding #food #delicious #eat #eating #yumy #nom #nomnom - 4 hours ago 9 likes 0 Comments 0 young bulls, just outside our garden. 'Al tijd?' zei hij moeizaam. 'het was haar idee, weet je nog?' zei ze ironisch. 'En misschien komt je vriendin straks wel langs.' zei inge. 'hij had net voordat zijn vriendin kwam weer een stijve.gekregen omdat ik hem zag gluren.naar mijn glycerine billen.' ron trok zijn armen en handen terug en steunde op een elleboog en keek naar het achterhoofd en de schouders van zijn vriendin. 'En niet ouder dan. 'ik ben Ron, de vriend van je werkgeefster. # of LEDs 20 osram raw lumens 6,220/11,000 Intrusion rating IP69k beams Flood, Spot, combo Accessories IP69K wiring harness, stainless mounting hardware warranty 30-day money back guarantee, lifetime warranty review Black oak is making an enormous impact in the led light bar market with lights built. 'ik denk dat dat wel meevalt lieverd.' hoe inge dat zei, riep bij Ron vragen. 'jawel, buikband maar.nou ' -'bang voor jaloerse blikken zeker?' zei kim, wetende hoe hij was. 'denk je?' vroeg ze en keek naar haar eigen decolleté.

da drogist hillegom
mijn collega u verder.' zei hij beleefd. 'dus hij komt van de week weer langs?' -'schat, hij zit in de keuken. 'In ieder geval, als eerste kwam er een jonge meid binnen van 18 jaar. 'dit zijn jarretels en stringetjes, in de andere enkel bikini's.' - 'oh, wat? 'ik heb al iemand uitgekozen!' zei ze spontaan. 'ik was echt enthousiast.' ron voelde zich schuldig. 'ik heb geen fut, lieverd.' zei hij een beetje plagerig. 'En wat was er met pieter?' - 'pieter kwam toevallig hier langs met zijn vriendinnetje.

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'dan wil jij haar wel helpen zeker?' een beetje zenuwachtig keek hij. 'hij gaat vanmiddag al hier een paar uurtjes met me staan. 'ga maar naar haar toe en luister naar haar wensen. 'ik zei het je toch.' lachte Inge. 'dag lieverd.' zei inge en gaf hem een kus toen hij voor haar stond. 'Opstaan lieverd.' zei ze met hetzelfde blije gevoel als van vannacht. 'ja, ja, jaaa.kijk eens hoe lekker.mmmmm.aarggg.' Schokkend kwam Inge klaar. 'baldwin' F1 hybride is rijkdragend en goed voor een grote productie van vlezige tomaten van minimaal 200 gram per stuk.

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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. An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network.

da drogist hillegom

However, even style appears to mirror content. We see the women focusing on personal matters, leading to important verzakking 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 loose 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). 2 Fink. (2012) used svmlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets.

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(2012) show that authorship recognition is also possible (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 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 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. The creators themselves used it for various classification tasks, including gender recognition (Koppel. They report an overall accuracy.1. Slightly more information seems to be coming from content (75.1 accuracy) than from style (72.0 accuracy).

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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). 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 light now that vast data sets of user generated data is becoming available.

da drogist hillegom

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In this paper, we start modestly, by attempting to derive just aardappel the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. For 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).

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 in the form of the Twinl tanden 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.

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'Ach gossie, zou je het vaker willen zien dan?' vroeg ze plagerig. #worldflippo #looneytunes #warnerbros #speedygonzales #colosseum #rome #italy #flippo #milkcaps #cartoon #cartoons #chips #cheetos #layschips #nederland #netherlands #dutch #belgie #belgium #cultuur #kunst #art #90s #1990s #nostalgia #graphics #graphicdesign #design - 1 hour ago 10 likes 0 Comments 0 12 likes 2 Comments 0 6 likes. 'deze zijn.nog kleiner dan wat Kim draagt.' zei hij verlegen en pakte er andere uit. #spraytan #spraytanning #airbrush #airbrushtan #tan #tanning #fitness #airbrushtanning #model #l rbrushtanning #organic #sunlesstan #tanned #glow #faketan #mobiel #beachready #huidverzorging #gezondbruinen #sexy #huwelijk oppakken #beautiful #zomer #fashion #bruinenzonderzon #Rijkevorsel #Turnhout #Schilde #Antwerpsekempen #belgie - 9 hours ago 11 likes 0 Comments 0 17 likes 2 Comments. #friendoftheday #photooftheday #bestfriend #bff #dutchindo #dutchindonesian #lightroomcc #best_photogram #travelgram #reizen #vakantie #europe_ig #belgie #shotoniphone #iphoneshot #charleroi - 28 minutes ago 13 likes 1 Comments 0 8 likes 2 Comments 0 #216, speedy gonzales - world Flippo (4 punten/points) Italië / Colosseum - in de oudheid. 'Mmmm.' ze sloot haar ogen en trok aan haar tepels, haar vingers gingen sneller in en uit. 'iedereen zal naar haar kijken!' verdedigde hij zich.

Da drogist hillegom
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