Query Expansion Demo - INFO556 - Fall 2025
Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models
An Information Retrieval engine for scientific papers – Lucene-powered with synonyms, wildcards, and smart query expansion.
Query Expansion & Re-Ranking for Biomedical Information Retrieval
Search Engine for Information Retrieval Coursework
A multi-phase Information Retrieval system built for IR2025, exploring query expansion techniques using BM25, WordNet, and Word2Vec. Includes full evaluation using TREC metrics (MAP, avgPre@k) and implemented in Python with Elasticsearch, Gensim, and NLTK.
LLM-based Listwise Reranker for Retrieval-Augmented Generation (RAG) over GitHub code repositories. Includes advanced retrieval, reranking, summarization, and evaluation with Recall@K.
Personalized information retrieval system for Q&A.
Personalized information retrieval system for Q&A.
Evaluating performance of BM25, Rocchio Model and Pseudo feedback algorithms for document retrieval
This project implements a financial research assistant tool that extracts insights from a PDF Report by generating augmented queries and performing semantic document search.
Query Expension for Better Query Embedding using LLMs
An advanced information retrieval system using TF-IDF, BM25, and query expansion (RM3) for accurate document search and ranking. Features NLP models (Elmo, BERT) for enhanced semantic understanding. Includes a Flask UI with Ngrok for Colab, allowing query input, result exploration, and performance evaluation.
This project implements a Document Retrieval System that integrates GPT-3.5-turbo for query expansion and answer generation. It fetches and ranks documents based on user queries, leveraging MongoDB for document storage, Redis for caching, and web scraping to keep documents updated. The system is designed to provide fast and accurate search results
Summarizing my findings on Query Expansion using Generative AI by evaluating and comparing the results of different query expansion methods.
Bachelor Thesis as part of the TU Delft Computer Science and Engineering Programme
Information Retreival Course Repository, 7th Semester, IITD, 2023-24
This project aims to analyze the sentiment about IKN (Ibu Kota Negara), the New National Capital in Indonesia. Data is obtained through crawling Twitter data related to IKN discussion topics. Furthermore, the data is analyzed using the SVM classification method by combining it with the Query Expansion technique to produce better model performance.
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