README.md

# Advanced Information Retrieval 2023 - Project - Group 20

This is our project for the course Advanced Information Retrieval 2023, TU Graz.
In this project, we use BERTopic to extract topic information from documents and queries and incorporate the topic information for re-ranking documents after the initial retrieval with BM25.

Authors: Gatternig Elias, Müller Hanna, Palasser Georg

[View Design Document](./design-document.pdf)

[View Presentation Slides](./slides.pdf)

## Dataset

We used the publicly available [CISI collection](https://ir.dcs.gla.ac.uk/resources/test_collections/cisi/) of the University of Glasgow, containing 1460 documents and 112 queries. It contains documents, queries and a "ground-truth" of query-document matchings.

## How to run

To run the first make sure all the requirements are met. Simply use the command "pip install -r requirements.txt" in your terminal. This will install all the packages required to run our code. Check if all libraries are installed. 

Secondly for running the "reranker-cosine.ipynb" notebook it is necessary to add the "GoogleNews-vectors-negative300.bin" word-embeddings file into the "models" -folder, they were not uploaded to the repository initially, because of the large size. However they can be downloaded [here](https://www.kaggle.com/datasets/leadbest/googlenewsvectorsnegative300). 

## Files

<u>**initial-retrieval.ipynb**</u>
Creating the intial-retrieval of the 76 queries using bm-25. Will retrieve 100 documents out of 1460 documents per query and save the results in _initial_retrieval_with_bm25_scores.pkl_. 

<u>**reranker-cosine.ipynb**</u>
Re-ranks the intial documents with the help of cosine similartiy and the pre-trained embeddings of _GoogleNews-vectors-negative300_. Will retrieve 50 documents out of the initial 100 per query and save the results in _reranker_embeddings_cosine_results.pkl_. 

<u>**reranker-bertopic.ipynb**</u>
Will create topics for all documents and queries and re-rank the intial retrieval. Will retrieve 50 documents per query and save the results in _reranker_bertopic_results_topic_model.pkl_. 

<u>**evaluation.ipynb**</u>
Takes the results of all three methods and calculates Recall@k, Precicsion@k, F1@k and nDCG@k. Will create plots for visualizing the results.