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Classifying human rights violations with deep multi-label co-training.

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Evolution. Blum’s Co-training – Running two classifiers over a given view to learn and classify content given a small labelled set Multi-labelled co-training – Co-training was extended to support multi-labels Content based Co-training – Co-training was extended to use matrix factorization Co-training with recommendations – To solve cold start problem Deep multi-label co-tranining.

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Method. Uses two neural networks to train on different views of generated samples to calculate similarity in the probability distribution of predicted outcomes. Cumulative loss Multi-label sigmoid cross-entropy – for the multi label classification Kullback-Leibler divergence - to find out how the two view differ from each other Noise sigmoid cross-entropy – Regularizing the added noise Adds noise to keep the classifier from being affected by it during prediction. words with given probability; replacing words by a filler token with given probability; swapping words up to a certain range..

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Results. 10% of labelled set. o-t Ikrvised multi-la Tree SVM Naive Baves o-training multi-lab Decision Tree SVM Naive Baves n ng nuj ecas.on ree KNN SVM Naive Bayes x acto za n Co-training multi-label with similarity recommendations Decision Tree KNN SVM Naive Bayes mu t NN co-tr accuracy 0.71015873015873 0336796536796537 0.71448773"87735 0.71015873015873 0.4321548821548822 4.9090'*j909fM91E+016 0.4993265993265993 4.93434343434343E+016 0.7057239057239€Y, 0.695622895622896 0.730832130832131 0.7663343 0.6299807 0.72343244 6 error rate 0.27984126984127 0.463203463203463 0265512265512265 0.28984126984127 0.5678451 178451178 0.5065656565656567 O.304377104377104 0.269167869167869 0.2336657 0.3700193 0.2432(MY787 0.27656756 TABLE I: Classifier comparison on of the labeled samples.

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Results. With different thresholds of the labelled set.

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Future. The present study used the domain of human rights violations. However can be applicable where - The aim is to identify and ignore forms of misinformation has an initial small labelled Has minimal set of experts.