Session1 Visualisation and Summarisation Tutorials

Introduction

This repository is part of the UCREL Summer School (USS) 2024, showcasing a series of Jupyter notebooks focused on visualising various aspects of Natural Language Processing (NLP) using SpaCy and other tools. These tutorials are designed to help participants understand and apply techniques for text visualisation and summarisation, including named entity recognition, syntactic dependency parsing, and creating word clouds.

For more information about the UCREL Summer School, please visit: UCREL Summer School 2024.

Repository Contents

Notebooks

1. 1_Visualisation_SpaCy_Tutorial.ipynb: This notebook demonstrates how to use SpaCy for text visualisation, focusing on named entity recognition and syntactic dependency parsing.
2. 2_BigData_Visualisation.ipynb: This notebook shows how to create and visualise word clouds from text data.
3. 3_Text_Summariser.ipynb: This notebook demonstrates how to summarise text using NLP techniques.
4. ArabicNews_wordcloud.ipynb: This notebook demonstrates how to create and visualise word clouds for Arabic news.
5. Parliament_wordcloud.ipynb: This notebook focuses on visualising word clouds from parliamentary debates.

Folders

• Extra-NoteBooks: Contains additional Jupyter notebooks for specific visualisation tasks.
• Notebooks: Contains all the main Jupyter notebooks.
• data: Contains sample datasets used in the tutorials.
• static: Contains static files, such as images, used in the notebooks.

Installation and Setup

To run these notebooks, you need to have Python and Jupyter Notebook installed. You can follow these steps to set up the environment:

1. Clone the repository:

   git clone https://github.com/yourusername/NLP_Visualisation.git
   cd NLP_Visualisation

2. Create a virtual environment:

   python3 -m venv nlp_env
   source nlp_env/bin/activate  # On Windows use `nlp_env\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Start Jupyter Notebook:

   jupyter notebook

Usage

Open any of the notebooks in Jupyter and run the cells to see the visualisations and understand the various NLP techniques demonstrated. Each notebook contains detailed instructions and explanations to guide you through the process.

Contributing

If you would like to contribute to this repository, please fork the repository and submit a pull request with your changes.

License

This repository is licensed under the MIT License. See the LICENSE file for more information.

Acknowledgements

This work is part of the UCREL Summer School (USS) 2024.

For more information, visit UCREL Summer School 2024.