Maar algen gaan er niet van dood. Het geeft wel wat verlichting, maar de alg komt daarna snel weer terug. Per liter lijkt azijn goedkoop, maar je bent er veel van nodig, moet het vaak gebruiken en officieel is het verboden om te gebruiken als bestrijdingsmiddel. Natuurlijk zijn er wel middeltjes op de markt die ook beweren dat ze groene aanslag tegels verwijderen zonder schrobben. En dat klopt ook deels. Alleen zijn ze niet zo effectief als biogroen. Je bent daardoor meer en vaker van deze middelen nodig, waardoor het je extra werk en vooral ook extra geld kost. (Lees daarover meer in het artikel.

groene aanslag tegels verwijderen zonder schrobben langere duur. Het kost bovendien veel energie om het water te verwarmen. En elke dag de aardappels buiten afgieten is ook niet voor de hand liggend. Azijn is een zuurmiddel waar planten van doodgaan.

En dan u kun je weer gaan boenen, schrobben of met de hogedrukspuit aan de slag. Biogroen is de oplossing voor groene aanslag verwijderen zonder schrobben! Biogroen is simpel aan te brengen met een nevelspuit. Na een paar dagen verdwijnt de groene aanslag en komt het de eerstkomende maanden ook niet meer terug. Groene aanslag verwijderen doe je met biogroen! Groene aanslag tegels verwijderen zonder schrobben, de alternatieven. Voor groene aanslag tegels verwijderen zonder schrobben zijn behandeling er owns eigenlijk geen echte alternatieven voor biogroen. Schrobben, boenen en de hogedrukspuit kosten veel tijd en zijn niet effectief op de langere termijn. Dat geldt ook voor verschillende huismiddeltjes die weleens genoemd worden. Zout bijvoorbeeld doodt wel plantjes, maar geen algen. Zelfs niet bij hoge concentraties.

groene aanslag tegels verwijderen zonder schrobben

Biomos bestellen, groene aanslag van

Groene aanslag tegels verwijderen zonder koop schrobben. Groene aanslag op tegels verwijderen. Groene aanslag op tegels verwijderen, betekende vroeger schrobben of later de hogedrukspuit. Schrobben is een zware en laser langdurige klus. En ook het werken met de hogedrukspuit kost de nodige tijd, en niet te vergeten water en energie. Maar dat is allemaal niet zo erg als het het gewenste resultaat op levert. En dat doet het dus helaas niet. De groene aanslag van alg is wel even weg, maar steekt al snel de kop weer.

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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. 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.

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.

Their products 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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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). 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.

Groene aanslag op tegels en mos verwijderen, cleanipedia

(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 dieet 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.

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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 europe 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.

In this paper, we start modestly, by attempting to mask 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. 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).

Hoe kan ik tegels schoonmaken en groene aanslag verwijderen?

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 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 haut 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.

Groene aanslag tegels verwijderen zonder schrobben
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