Vector Space Retrieval Model

This project implements a Vector Space Model (VSM) for Information Retrieval using the Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme. The VSM is a classical approach to document retrieval in information retrieval systems, where documents and queries are represented as vectors in a high-dimensional space, and similarity between documents and queries is computed using cosine similarity.

Features

Supports free word queries.
Utilizes the TF-IDF weighting scheme for retrieval.
Ranks document based on the similarity scores calculated using Cosine Similarity.
Provides a simple GUI interface for user interaction.

Getting Started

To run the information retrieval system, follow these steps:

Ensure you have Python 3.12 installed.
Install NLTK and tkinter 'pip install NLTK' and 'pip install tkinter' repectively.
Make sure Stopword-List.txt and the Research Paper directory containing all the documents is in your current working directory.
Update the path in the ASSET PATH to point to the asset folder.
Run the files in an IDE.
Run this command to download the tokennizer nltk.download('punkt')
Run the 'weights_calculation.py' script first using 'python weights_calculation.py' to create and save the weights.
Then run the 'query_processing.py' using 'python query_processing.py' for queries.
Use the tkinter GUI interface to input queries and press 'Search' button to retrieve the required document IDs.
Press the 'Exit' button to exit the program.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

This project was inspired by information retrieval concepts and algorithms.
Special thanks to the developers of NLTK for providing essential natural language processing tools.
Special thanks to Parth Jadhav for developing TkinterDesigner that was used for making the GUI.

zohaibterminator/vector-space-retrieval-model

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