Humor detection in edited news headlines: predicting humor ratings for news headlines with atomic change

Yiming Tang

doi:10.1117/12.2634647

4 May 2022 Humor detection in edited news headlines: predicting humor ratings for news headlines with atomic change

Yiming Tang

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217214 (2022) https://doi.org/10.1117/12.2634647
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

Humor detection has recently become one of the most popular topics in NLP. Now existing humor datasets are mainly designed to analyze whether most of the text is funny. SemEval-2020 Task 7 was given to assess humor in edited news headlines. Our task is to analyze how one-word or one-phrase editing could convert a text from non-funny to funny by predicting humor ratings for edited news headlines. We replace the regression task with the three-category and two-category classification tasks. We use a pre-trained BERT model. The results suggest that humor of headlines with atomic editing are not significantly detected by our model for tasks including regression, three category classification, and two-category classification. We discuss the process of how we update our model and possible explanations of why the model is not significant. Moreover, we analyze features under each label in the dataset and data distribution in the dataset.

Citation Download Citation

Yiming Tang "Humor detection in edited news headlines: predicting humor ratings for news headlines with atomic change", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217214 (4 May 2022); https://doi.org/10.1117/12.2634647

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