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Felix Jan Michael Mucha 2025-02-16 23:15:33 +01:00
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@ -4,12 +4,11 @@ This repository contains the necessary scripts, data, and notebooks for analyzin
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### Objektive ### Objektive
Leverage advanced NLP techniques (LSTM, CNN, BERT, and Transformer) to analyze text data and build an application that predicts humor ratings. Leverage advanced NLP techniques (CNN, BERT, and Transformer) to analyze text data and build an application that predicts humor ratings.
### Research Question ### Research Question
... - **Can Deep neural networks predict humor ratings with an RMSE greater than or equal to the baseline of 0.8609 ?**
## Data Source ## Data Source
The data is sourced from the SemEval-2021 Task 7: The data is sourced from the SemEval-2021 Task 7:
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### 4. Classification and Regression ### 4. Classification and Regression
While the primary goal of the project is to predict the numerical humor rating (regression task), we also experiment with classification models for humor detection (e.g., humor vs. non-humor) While the primary goal of the project is to predict the numerical humor rating (regression task), we also experiment with classification models for humor detection (e.g., humor vs. non-humor)
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## Research References
### Key Papers in Humor Detecion
1. **Humor recognition using deep learning.” Humor recognition using deep learning** (https://aclanthology.org/N18-2018.pdf)
2. **ADVERSARIAL TRAINING METHODS FOR SEMI-SUPERVISED TEXT CLASSIFICATION** (https://arxiv.org/pdf/1605.07725)
3. **Humor Detection: A Transformer Gets the Last Laugh** (https://aclanthology.org/D19-1372/)
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## Summary