(Uit de analyse bleek in details dat ze, zonder het te weten, haar eerste geliefde fantaseerde: gestalte, haarkleur enz. "Schrijf ook verklaringen van collega's op en bewaar alles zorgvuldig zegt sectorbestuurder Hans Crombeen van fnv bouw infra naar aanleiding van het asbestschandaal op het Utrechtse bedrijventerrein Lageweide. (Ik had het hem enigszins voorspeld, maar toch kwam het nog sneller dan verwacht.) Of het amalgaam hier schuldig aan is zullen we nooit weten, er werden twee stens verwisseld die reeds gedeeltelijk verstopt waren. (en) h jick, gl zornberg, ss jick, s seshadri, da drachman. ( nooit harder ontgiften dan het zwakste orgaan aan kan!) Tot het onhoudbaar was en er iets moest gebeuren om het beter te krijgen. ( jc ). "Vrouwen bellen veel minder vaak met hun vader tijdens deze vruchtbare dagen en ze hangen sneller op, wanneer ze door hem gebeld worden volgens Martie haselton, een ucla professor in communicatie.

marinade vlekken verwijderen de actuele positie van pijn verschilt van het beeld dat afkomstig is van de hersenen en dit wordt aangeduid als afgeleide pijn. (1901 On lines and planes of closest fit to systems of points in space, philosophical Magazine 2 (11 pp Pennebaker,. (Juola 2008) and (Koppel. (we zijn ons goed bewust dat de identiteitscrisis van de student materialistisch alleen maar verklaard kan worden uit een samenspel van de volgende factoren,.

"de zenuw die deze pijnreceptoren naar de hersenen voert door de nervus vagus. (32-times) * Eat only half of your stomach. "Omdat het ontstaan van nierstenen beinvloed wordt door een verschil in levensstijl en andere gezondheidsgerelateerde factoren, is de ware impact van oestrogeentherapie op het ontstaan van nierstenen, moeilijk te concluderen vanuit obsevatiestudies." lees verder Seth Gebrek aan carotenoïde kan de gezondheid van de vrouw aantasten. "We need to explain to people that just like putting on condoms, you have to take this precautionary measure to make the product be as safe as it can. (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. (2014) did a crowdsourcing experiment, in which they asked human participants to guess the gender and age on the basis of 20 to 40 tweets. "Moge hij vanuit zijn rijke luister uw innerlijke wezen kracht en sterkte schenken door zijn geest, zodat door uw geloof Christus kan gaan wonen in uw hart, en u geworteld en gegrondvest blijft in de liefde." (Efeziërs 3:16-17). "Moge god, die ons hoop geeft, u in het geloof geheel en al vervullen met vreugde en vrede, zodat uw hoop overvloedig zal zijn door de kracht van de heilige geest." (Romeinen 15:13). (Bio) "Boer zoekt hulp" zullen we maar zeggen. (denk aan Alzheimer of Sclerose of Parkinson en alle andere problemen havermout van het zenuwstelsel).

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(The "Serve size (g column is the serving size in grams for calculating the glycemic load; for simplicity of presentation I have left out an intermediate column that shows the available carbohydrates in the stated serving sizes.) take, watermelon as an example of calculating glycemic. (Alsof door het uitspreken van het wij (er)kennen geen leiders de afhankelijkheid van de leiders opgeheven zou zijn.) Werd in het begin van de antiautoritaire studentenbeweging een positieve identificatie opgebouwd met het lijdende en na verloop van tijd met het strijdende vietnamese volk, later ontwikkelde. (kite kan liat konten, bukan setia pada person ya tak ya tak ) Untuk yang kesekian kali, kekuatan pasar Ina di exercise setelah bbm yang berhasil dimenangkan akankah sekarang film impor mau ditundukkan? (12) Aanbevolen dagelijkse hoeveelheid: 2 liter water per dag. 'Ongezonde' maaltijden omtoveren tot een heerlijke gezonde maaltijd, het kan echt! "Wees steeds bescheiden, zachtmoedig en geduldig, en verdraag elkaar uit liefde." (Efeziërs 4:2). "New car smell" for example, comes from toxic chemicals being released from plastics, foams and calorieën fabrics used to make steering wheels, dashboards and seats. 0,3 gram proteïne en 0 gram vet. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English.

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172 3 For Tweets in Dutch, we first look at the official user interface for the Twinl data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches. 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. If no cue is found in a user s profile, no gender is assigned. The general quality of the assignment is unknown, but in the (for this purpose) rather unrepresentative sample of users we considered for our own gender assignment corpus (see below we find that about 44 of the users are assigned a gender, which is correct. Another system that predicts the gender for Dutch Twitter users is TweetGenie that one can provide with a twitter user name, after which the gender and age are estimated, based on the user s last 200 tweets. The age component of the system is described in (Nguyen. The authors apply logistic and linear regression on counts of token unigrams occurring at least 10 times in their corpus. The paper does not describe the gender component, but the first author has informed us that the accuracy of the gender recognition on the basis of 200 tweets is about 87 (Nguyen, personal communication).

(2011) attempted to recognize gender in rugpijn 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. 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. When adding more information sources, such as profile fields, they reach an accuracy.0.

Keukenweetjes - blogs - seniorenNet


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). 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.

Gender Recognition on Dutch

(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 lever 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.

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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). 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 uden 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.

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 kopen relations, profile photos, and the text of the tweets. 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 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.

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U heeft een blog bezocht dat niet (meer) bestaat, of dit blog maakt gebruik van een design (skin) dat niet (meer) bestaat of beschadigd. We sturen u over 10 seconden naar de begin pagina van. 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 data set: a daily updated collection that probably contains at least 30 of the dutch public tweet production since 2011 (Tjong Kim Sang betekenis 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.

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