Papers with humor recognition
Humor Recognition Using Deep Learning (N18-2)
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| Challenge: | Humor is an essential but most fascinating element in personal communication. |
| Approach: | They propose a convolutional neural network with extensive filter size and filter number to increase the depth of networks. |
| Outcome: | The proposed model outperforms existing models on accuracy, precision and recall . the proposed model can learn to distinguish between humorous and nonhumorous texts . |
Modeling Sentiment Association in Discourse for Humor Recognition (P18-2)
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| Challenge: | Existing work on sentiment information is limited to the number of emotional words. |
| Approach: | They propose to model sentiment association between discourse units to indicate how punchline breaks expectation of setup. |
| Outcome: | The proposed model shows that discourse relation, sentiment conflict and sentiment transition are effective indicators for humor recognition. |
Mining Effective Features Using Quantum Entropy for Humor Recognition (2023.findings-eacl)
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| Challenge: | Existing studies on humor recognition do not understand the mechanisms that generate humor. |
| Approach: | They propose to use quantum entropy to represent the semantic uncertainty of the setup and punchline as features for humor recognition. |
| Outcome: | The proposed features are more effective than baselines for recognizing humorous and non-humorous texts on the SemEval2021 task 7 dataset. |
Exploiting Syntactic Structures for Humor Recognition (C18-1)
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| Challenge: | Using syntactic structure features, we find humor recognition is a kind of style . |
| Approach: | They propose to exploit syntactic structure features to enhance humor recognition . they find syntastic structure features consistently correlate with humor . |
| Outcome: | The proposed method achieves significant improvements compared with baselines. |
Commonality and Individuality! Integrating Humor Commonality with Speaker Individuality for Humor Recognition (2025.naacl-long)
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| Challenge: | Current methods for humor recognition focus on one aspect of humor commonalities, ignoring the multifaceted nature of humor. |
| Approach: | They propose a commonality and individuality incorporated network for humor recognition that integrates multifaceted humor commonalities with speaker individuality. |
| Outcome: | The proposed model integrates multifaceted humor commonalities with speaker individuality to deepen the understanding of humor expressions. |
Large Dataset and Language Model Fun-Tuning for Humor Recognition (P19-1)
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| Challenge: | Humor recognition datasets contain only English texts and focus on puns. |
| Approach: | They collected a dataset of jokes and funny dialogues in Russian and complemented them carefully with unfunny texts with similar lexical properties. |
| Outcome: | The proposed method is based on the universal language model finetuning and has an F1 score of 0.91 on a test set. |
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition (D19-1)
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| Challenge: | Existing methods for humor recognition require a large amount of training data with labels to learn effective features. |
| Approach: | They propose a tensor embedding method that can extract lexical humor features for continuous humor recognition by using word-word co-occurrence to encode contextual content of documents, and then decompose the tenor to get corresponding vector representations. |
| Outcome: | The proposed method achieves a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B, but without the need for any external training corpus. |