88 lines
3.5 KiB
Markdown
88 lines
3.5 KiB
Markdown
# ANLP_WS24_CA2
|
|
This repository contains the necessary scripts, data, and notebooks for analyzing and modeling the linguistic and structural features of humor in jokes. The project focuses on leveraging NLP techniques to analyze humor in text data, and aims to predict the humor score numerically using regression models.
|
|
|
|
---
|
|
|
|
### Objektive
|
|
Leverage advanced NLP techniques (LSTM, CNN, BERT, and Transformer) to analyze text data and build an application that predicts humor ratings.
|
|
|
|
### Research Question
|
|
|
|
...
|
|
|
|
|
|
## Data Source
|
|
The data is sourced from the SemEval-2021 Task 7:
|
|
It contains a dataset of humor and offense ratings for jokes. The jokes are annotated with a humor rating on a scale from 0 to 4.
|
|
|
|
- Traindata: HaHackathon.https://homepages.inf.ed.ac.uk/s1573290/data.html -> associated paper: https://aclanthology.org/2021.semeval-1.9.pdf#:~:text=HaHackathon%20is%20the%20first%20shared%20task%20to%20combine,its%20average%20ratings%20for%20both%20humor%20and%20offense
|
|
- Testdata: Since no test data was available, the traindata also was used as test data and divided into test, train and validation data
|
|
|
|
|
|
## Data embeddings
|
|
- gloVe 6B tokens: https://nlp.stanford.edu/projects/glove/
|
|
|
|
|
|
### Preprocessing Steps
|
|
**1. Load and clean data:** The data set is loaded and all rows with missing humor_rating values are removed. In addition, the target variable for the humor rating is extracted.
|
|
|
|
**2. text embeddings:** Pre-trained GloVe embeddings are loaded and converted into a matrix that can be used for modeling.
|
|
|
|
**3. data splitting:** The data set is split into training, test and validation data to train and evaluate the models later.
|
|
|
|
**4. ensemble data indices:** Various methods for creating data indices are provided to prepare the training data for ensemble methods.
|
|
---
|
|
|
|
|
|
## Repository Structure
|
|
|
|
- **`data/`**: Contains the dataset `hack.csv`, which includes raw joke data.
|
|
- **Notebooks**:
|
|
- Used for data analysis and visualization.
|
|
- represent the models
|
|
|
|
---
|
|
## Getting Started
|
|
|
|
### Install Requirements
|
|
Run the following command to install the required dependencies:
|
|
|
|
```bash
|
|
pip install -r requirements.txt
|
|
```
|
|
|
|
## Preprocess Data
|
|
This is carried out automatically when the models are executed
|
|
|
|
## Workflow
|
|
### 1. Preprocessing
|
|
The text data is cleaned and transformed into formats suitable for analysis. The preprocessing steps include tokenization, stopword removal, and lemmatization.
|
|
|
|
### 2. Model Training
|
|
Various machine learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), BERT, and Transformers, are trained to predict the humor rating of jokes based on their linguistic features.
|
|
|
|
### 3. Model Evaluation
|
|
The trained models are evaluated to determine their performance in predicting humor ratings. Metrics such as RNSE (Root Mean Squared Error) and R² scores are used to assess the models.
|
|
|
|
### 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)
|
|
|
|
---
|
|
|
|
## 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/)
|
|
|
|
|
|
|
|
---
|
|
## Summary
|
|
|
|
|