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We propose a novel neural network model for machine reading, DER Network, which explicitly implements a reader building dynamic meaning representations for entities by gathering and accumulating information around the entities as it reads a document. Evaluated on a recent large scale dataset (Her-mann et al., 2015), our model exhibits better results than(More)
In this paper, we compare feature-based and Neural Network-based approaches on the supervised stance classification task for tweets in SemEval-2016 Task 6 Subtask A (Moham-mad et al., 2016). In the feature-based approach , we use external resources such as lexicons and crawled texts. The Neural Network based approach employs Convolutional Neu-ral Network(More)
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