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Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning

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Figure

Table 3: Agreement measures for the humour dataset.
Table 5: Task 1.Spearman’s ρ between GPPL andgold-standard scores produced by BWS when trainedwithout features.
Figure 4 shows the results of Task 3,with the rightmost points corresponding to the
Figure 5: Task 4. Spearman’s ρtraining instances (subsampled bytion for rank prediction on pair or by annota-) with decreasing data sparsity (p ≪ 0.01).

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