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twitter data 0.0021720900000000002
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annotation projects 0.00213329
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data sets 0.001971332
full data 0.001961221
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media data 0.001873413
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french data 0.0018409910000000002
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tag pairs 0.001386192
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media noun 9.876099999999999E-4
linguistic intuitions 9.87552E-4
car noun 9.85459E-4
linguistic theories 9.76279E-4
linguistic motivation 9.76279E-4
single annotator 9.729059999999999E-4
tonight noun 9.676649999999999E-4
annotator disagreements 9.58247E-4
noun distinctions 9.527E-4
text types 9.32106E-4
response model 9.14994E-4
english twitter 9.128140000000001E-4
set con 9.01862E-4
gold standard 8.97199E-4
same disagreements 8.727709999999999E-4
training section 8.72135E-4
annotator choices 8.62906E-4
possible tags 8.46583E-4
features 8.42045E-4
annotator reliability 8.418459999999999E-4
learning 8.39529E-4
individual annotators 8.38475E-4
right analysis 8.35779E-4
classification results 8.32622E-4
confusion types 8.285969999999999E-4
batable cases 8.22623E-4
majority voting 8.224720000000001E-4
metaphoric cases 8.2076E-4
corpus 8.16118E-4
english text 8.15882E-4
results figure 8.14705E-4
twitter posts 8.1243E-4
errors 8.10536E-4
text corpora 8.09084E-4
gold items 8.08116E-4
lay annotators 8.03996E-4
annotated confusion 7.99866E-4
quantitative analysis 7.94253E-4
downstream tasks 7.859060000000001E-4
empirical analysis 7.772289999999999E-4
annotations 7.75149E-4
confusion pairs 7.67114E-4
ing pca 7.60329E-4
structed annotators 7.57909E-4
specific theory 7.54682E-4
