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This repository hosts the implementation of a Simple Search Engine designed for efficient information retrieval. The project encompasses several stages from data collection to evaluation, ensuring a comprehensive approach to search and retrieval.
This project creates a basic search engine for text documents, covering data collection, preprocessing, indexing, query processing, expansion, UI development, and performance evaluation. Its goal is to efficiently retrieve relevant information from the document collection.
This repository contains the code for a research project that implements and evaluates local word embeddings based on co-authorship and citations for query expansion in PyTerrier on the TREC-Covid dataset.
This project constructs an ad-hoc information retrieval system using the π·ππ ππππ100 dataset with PyTerrier. NLTK handles query processing, including tokenization and stemming. BM25 ranking is used with enhancing performance through optimizations. The system features a minimalistic tkinter-based user interface for an intuitive experience.