Summary of Systematic Literature Review: Computational Approaches For Humour Style Classification, by Mary Ogbuka Kenneth et al.
Systematic Literature Review: Computational Approaches for Humour Style Classification
by Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a systematic literature review (SLR) on computational techniques for humor style analysis, drawing from studies on binary humor and sarcasm recognition. The authors survey various approaches, datasets, and evaluation metrics used in these related tasks, highlighting their relevance to humor style classification. They identify features such as incongruity, sentiment, ambiguity detection, acoustic nuances, visual cues, contextual insights, and more, which can be leveraged for humor style analysis. The review also explores machine learning paradigms, neural network architectures, transformer-based models, and specialized models tailored to humor’s nuances. By examining these computational techniques, the SLR aims to determine research gaps and outline promising directions for future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about studying how computers can understand different types of humor. Humor can be helpful or hurtful, depending on the type. Researchers have been working on ways for computers to recognize certain types of humor, like sarcastic comments. This study looks at what others have done in this area and finds common approaches, tools, and methods used. It also identifies areas where more research is needed. |
Keywords
* Artificial intelligence * Classification * Machine learning * Neural network * Transformer
