This repo contains reports with code in the form of jupyter notebooks for assignments 2 and 3 in the Chalmers course Machine Learning for Natural Language Processing (DAT450). The topics are as follows:
- Assignment 2: Topic modeling with latent Dirichlet allocation and Gibbs sampling
- Assignment 3: Word sense disambiguation with different neural architectures (linear, CNN, LSTM, bi-LSTM).
In addition, code and a report for the independent project can be found here. In this project, we looked at different ML techniques for automatic text summarization.