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GraPHIA: a computational model for identifying phonological jokes

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Abstract

Currently in humor research, there exists a dearth of computational models for humor perception. The existing theories are not quantifiable and efforts need to be made to quantify the models and incorporate neuropsychological findings in humor research. We propose a new computational model (GraPHIA) for perceiving phonological jokes or puns. GraPHIA consists of a semantic network and a phonological network where words are represented by nodes in both the networks. Novel features based on graph theoretical concepts are proposed and computed for the identification of homophonic jokes. The data set for evaluating the model consisted of homophonic puns, normal sentences, and ambiguous nonsense sentences. The classification results show that the feature values result in successful identification of phonological jokes and ambiguous nonsense sentences suggesting that the proposed model is a plausible model for humor perception. Further work is needed to extend the model for identification of other types of phonological jokes.

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Notes

  1. It is possible that the computation of thresholds is data set specific and may not generalize very well to other data sets. Hence, we also used a two-layer feed forward neural network to classify the given text input into the relevant classes which does involve a fixed threshold. The network was trained with the Levenberg–Marquardt algorithm and the simulation was implemented in MATLAB. Half the data set used for training and the other half of the data set used for testing. An accuracy of 98.3% was obtained with the neural network classifier indicating that the novel features can be used to identify normal sentences, homophonic puns and the two types of nonsense ambiguous sentences even without using pre-assigned explicit thresholds.

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Authors and Affiliations

  1. Centre for Behavioural and Cognitive Sciences, University of Allahabad, Allahabad, 211-002, India

    Narayanan Srinivasan & Vani Pariyadath

Authors
  1. Narayanan Srinivasan
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  2. Vani Pariyadath
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Corresponding author

Correspondence to Narayanan Srinivasan.

Appendix: List of sentences for which feature values are shown in Table 1

Appendix: List of sentences for which feature values are shown in Table 1

  • List of jokes

  • [Pisa] Italian building inspectors in Pisa are leanient.

  • [Criminal] A criminal’s best asset is his lie ability.

  • [Dentist] Be kind to your dentist because he has fillings too.

  • [Gold] You can make gold soup by putting in 24 carrots.

  • [Atheism] Atheism is a non-prophet organization.

  • List of ambiguous sentences

  • [Car] The car’s mane was very shrewd.

  • [Key] When they found the key a kettle bloomed.

  • [Steel] A blue steel waited calmy.

  • [Breakfast] She ate my breakfast role.

  • [Cruise] The cruise finished construction on the building.

  • [Flee] There was a flee on the dog’s back.

  • List of normal meaningful sentences

  • [Werewolf] Werewolves are fictitious creatures like elves.

  • [Deer] Not all animals are as agile and graceful as a deer.

  • [Bee] A bee stung her on the cheek.

  • [Plate] She washed the plates with soap and water.

  • [Cabbage] Cabbages are like lettuce because they are both vegetables.

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Srinivasan, N., Pariyadath, V. GraPHIA: a computational model for identifying phonological jokes. Cogn Process 10, 1–6 (2009). https://doi.org/10.1007/s10339-008-0221-3

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  • DOI: https://doi.org/10.1007/s10339-008-0221-3

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