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An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking

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Figure

Figure 1: The overall framework of Interpretable Neu-ral Network for Relevant Emotion Ranking (INN-RER).
Table 1: Statistics for the three corpora used in our ex-periments.
Table 3: Experimental results of the proposed approach and the baselines. ’PL’ represent Pro Loss, ’HL’ representsHamming Loss, ’RL’ represents ranking loss, ’OE’ represents one error, ’AP’ represent average precision, ’Cov’cates “the smaller the better”,
Figure 2: The top topic words under each emotion category from the News corpus.
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