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Revisiting the Importance of Encoding Logic Rules in Sentiment Classification

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Figure

Figure 1: Variation in models trained on SST-2 (sentence-only). Accuracies of 100 randomly initialized models areplotted against the number of epochs of training (in gray),along with their average accuracies (in red, with 95% confi-dence interval error bars
Figure 2: Comparison of the accuracy improvements reported in Hu et al. (2016) and those obtained by averaging over 100random seeds
Figure 3: Heat map showing the cosine similarity between pairs of word vectors within a single sentence
Table A1: Examples of neutral sentences for a threshold of 0.66
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