This project is built as a part of the Information Retrieval Course. The task involved implementing a Vector Space Model by calculating a tf-idf score for all the terms (words) present in the documents.
Preprocessed the text from the documents in terms of tokenization in which case folding, stop-words removal, and lemmatization is done. Then tf-idf score is calculated for all words present in documents. And vectors are created.
The vector space model is an algebraic model for representing text documents as vectors of identifiers. It is used in information filtering, information retrieval, indexing, and relevancy rankings.
Frontend for Vector Space Model of IR is developed using React, Material-UI, and CSS.
Backend is developed on Flask: Vector-Space-Model-Information-Retrieval-Backend
Installation and Setup Instructions Clone down this repository. You will need node and npm installed globally on your machine.
Installation:
npm install
To Run Test Suite:
npm test
To Start Server:
npm start
To Visit App:
localhost:3000