By 1671 the traveller Evliya Çelebi documents "a town to the basic, an hour or so in the sea, surrounded by strong walls two-storeys high, moated on all sides, with a few distinctive neighbourhoods In the walls".
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At this period the patron god of the town was Sandon, of whom a considerable monument existed at Tarsus at the least right up until the 3rd century AD. Coins confirmed Sandon standing over a winged and horned lion, and it is currently thought probably that the Lion of Saint Mark over the pillar from the Piazza San Marco in Venice was in origin a winged lion-griffin from this type of monument at Tarsus.
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three For Tweets in Dutch, we 1st look at the Formal person interface for your TwiNL details set, Between other issues, it demonstrates gender and age statistics for that users making the tweets found for consumer specified lookups. These stats are derived within the consumers profile information and facts By means of some heuristics. For gender, the system checks the profile for around a hundred and fifty common male and one hundred fifty frequent feminine initial names, and for gender linked words, like father, mother, wife and partner. If no cue is present in a consumer s profile, no gender is assigned. The final high quality on the assignment is not known, but in the (for this goal) somewhat unrepresentative sample of users we regarded as for our possess gender assignment corpus (see down below), we find that about 44% of your customers are assigned a gender, that's accurate in about 87% of your instances. Yet another process that predicts the gender for Dutch Twitter buyers is TweetGenie (), that 1 can provide that has a Twitter person name, after which the gender and age are approximated, dependant on the user s final 200 tweets. The age part of the program is described in (Nguyen et al. 2013). The authors implement logistic and linear regression on counts of token unigrams happening at least ten instances in their corpus. The paper isn't going to describe the gender ingredient, but the primary author has educated us that the precision with the gender recognition on The idea of two hundred tweets is about 87% (Nguyen, personalized communication). 3 In afterwards experiments, Nguyen et al. (2014) did a crowdsourcing experiment, through which they asked human participants to guess the gender and age on The idea of 20 to 40 tweets. When utilizing a bulk vote to depict the group s feeling, the crowd s notion of your gender on The idea of the tweets coincided with the actual gender in about 84% with the scenarios. The summary is not really much, nonetheless, that people may also be not best at guessing age on the basis of language use, but alternatively that there is a distinction concerning the Organic along with the social id of authors, and language use is more likely to symbolize the social a single (cf.
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The terms of surrender of town authorized any Muslim who wished to depart with as lots of his possessions as he could have. Lots of people who remaining sooner or later settled, Based on al-Muqaddasi, at Baniyas. The vast majority of people who remained driving turned Christians, along with the local key mosque was either torn down or became a stable.[sixteen] The city remained underneath Byzantine rule until finally 1085.
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one Computational Linguistics within the Netherlands Journal four (2014) Submitted 06/2014; Revealed 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra Radboud University Nijmegen, CLS, Linguistics Summary On this paper, we investigate gender recognition on Dutch Twitter substance, using a corpus consisting of the entire Tweet creation (as far as existing inside the TwiNL data established) of 600 customers (recognised for being human individuals) about 2011 and We experimented with numerous authorship profiling procedures and a variety of recognition attributes, working with Tweet text only, if you want to find out how well they might distinguish in between male and woman authors of Tweets. We accomplished the most beneficial benefits, 95.five% correct assignment inside of a 5-fold cross-validation on our corpus, with Aid Vector Regression on all token unigrams. Two other device learning devices, Linguistic Profiling and TiMBL, come near this outcome, no less than once the enter is 1st preprocessed with PCA. 1. Introduction While in the Netherlands, We've a somewhat distinctive useful resource in the form with the TwiNL information established: a daily up-to-date collection that possibly contains at the least 30% on the Dutch general public tweet creation considering the fact that 2011 (Tjong Kim Sang and van den Bosch 2013). Nonetheless, as any selection which is harvested mechanically, its usability is decreased by an absence of reliable metadata. In cases like this, the Twitter profiles of the authors can be found, but these encompass freeform text in lieu of fixed details fields. And, obviously, it truly is not known to which degree the knowledge that's existing is true. The useful resource would turn out to be more helpful if we could deduce comprehensive and proper metadata from the different accessible info sources, including the supplied metadata, consumer relations, profile photographs, plus the text on the tweets.