Advanced Question Answering System with NLP and Graph Operations

This is a Python-based project that uses Natural Language Processing (NLP) and Graph Operations to answer questions, provide recommendations, and perform crowdsourcing tasks for movies. This project was done for the course Advanced Topics in AI at the University of Zurich.

Structure

The project is structured as follows:

• main.py: The main entry point of the application.
• usecases/: Contains the main logic for the application, including scripts for answer calculation, graph operations, and NLP operations.
• notebooks/: Contains Jupyter notebooks for various tasks such as Named Entity Recognition (NER), Natural Language Generation (NLG), crowdsourcing, and embeddings.
• test_usecases/: Contains unit tests for the usecases.
• models/: Contains the trained models used in the application.
• data/: Contains the data used in the application.
• lib/: Contains additional libraries used in the application.
• speakeasypy/: Contains the source code for the SpeakeasyPy library.