Roget's Thesaurus in the 21st Century

Introduction

Roget's Thesaurus is a widely used reference work for writers and students. It groups words into categories and provides a list of synonyms for each word. The first edition was published in 1852, and it has been updated many times since then. The most recent edition was published in 2019. Our task in this assignment is to investigate how these categories fare with the meaning of English words as captured by Machine Learning techniques, namely, their embeddings.

Project Structure

The project is organized as follows according to the poetry standard for our Assignment:

• scrapping.ipynb: The Jupyter notebook that contains the code to scrape the data from the website and save the embeddings of the words to a ChromaDB database. (1st and 2nd objectives)
• clust_embed.ipynb: The Jupyter notebook that contains the code for the one and two-level clustering of the embeddings. (3rd objective)
• cls_embed.ipynb: The Jupyter notebook that contains the code for the classification of the embeddings. (4th objective)

Note: We also have the scrapping_full_thesaurus.ipynb notebook that contains the code to scrape the full thesaurus from the website and save the embeddings of the words to a ChromaDB database. However, we did not use this notebook in the project. We only used the scrapping.ipynb because it produced similar results and was faster to run.

Lastly, the Notebook rogets_thesaurus.ipynb contains the assignment with the details of the project objectives (4 in total).

Dependencies for the Project

We will need to use libraries that are not included in the Python Standard Library which are handled using Poetry

In order to install the dependencies, we will need to run the following commands in the terminal

1. (If we haven't already installed Poetry)

pip install poetry

2. After we have installed Poetry, we will need to run the following command in the terminal

poetry install

3. (Optional) If we want to use the Jupyter Notebook with the virtual environment created by Poetry, we will need to run the following command in the terminal

poetry shell

4. After we have installed the dependencies, we will need to run the following command in the terminal

jupyter notebook

Ollama Mistral Model

In the clustering notebook, we will use the Mistral Chat model from Ollama. The model is not included in the repository and needs to be downloaded from Ollama's website. Ollama can be downloaded from the following link: Ollama

After downloading the model, we will need to pull it into our system using the following command in the terminal

ollama run mistral

and then we will need to run the following command in the terminal

ollama serve

for the model to be available for use in the clustering notebook.