This project implements a semi-supervised approach to classify UN speeches.

We have implemented this approach in 2 ways:

The method is best illustrated with the following diagram:

• Generate word embeddings using BERT Sentence Transformer
• Generate a graph using cosine similarity for edges and sentence as the node
• Generate embeddings using Node2Vec
• Train a Neural Network to classify into topics using graph embeddings.

The flowchart illustrating this approach:

• Generate word embeddings using BERT Sentence Transformer
• Train a Neural Network (N1) on these embeddings
• Pseudo-label data using N1
• Stack labelled and pseudo-labelled data
• Train a more complex Neural Network (N2)

Read the Detailed Report for further information.

The dataset for this project contains approximately 2 million sentences from UN General Debate speeches held from 1970 to 2016.

A sample of the dataset is saved as csv files in this repo. The original is publicly available on the Harvard Dataverse and on my GDrive.

1. Download the dataset from the above GDrive link and unzip it into data folder
2. Execute the preprocess.py file
3. Execute either:

   1. approach1.py to train the model using the first approach

      OR

   2. approach2.py to train the model using the second approach

1. Download the contents from the above GDrive link
2. Put the csv files in the data folder
3. Put everything else in the weights folder
4. For inference, execute inference2.py to use the saved weights from the second approach. The program will ask for an input sentence and will output the predicted class.

• pandas==2.2.1
• numpy==1.26.4
• nltk==3.8.1
• maptlotlib==3.8.3
• sentence_transformers==2.5.1
• tensorflow==2.16.1
• gensim==4.3.2
• node2vec==0.4.6

• Yash Jain
• Abhinav Shukla