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Towards a General Framework for Humor Generation from Rated Examples

The document discusses a model related to humor, focusing on various factors such as relatedness, dissimilarity, ambiguity, and uncommonness. It highlights the importance of these elements in evaluating humor performance. The emphasis is on understanding the relationships between different concepts in humor assessment.

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Z PartOf X Z CapableOf Y …
Humour
pm_analogy_model relatedness(X, Z), relatedness(Y, Z), dissimilarity(X,Z), ambiguity(Z), uncommonness(Z
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Humour
Towards a General Framework for Humor Generation from Rated Examples

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Towards a General Framework for Humor Generation from Rated Examples

Editor's Notes

  • #47 - Created platform - Rate & Create (& Analyse)
  • #48 During training data colletion ~50% of the ratings ~70% of the jokes Agreement -> DIFFERENT appreciations
  • #49 Computational JUDGER required - NEED TO CREATE & TRAIN
  • #51 Generated 100 jokes to JokeJudger This = 9500 ratings & markings First three categories: all jokes Classifier > Regression In half of the times, the jokes of the computer are equally good as human jokes. CAN BE FILTERED BY HUMANS!