INFORMATION
RETRIEVAL
FROM
NOISY
DOCUMENTS

Abstract

This project entails building an AI-powered Information Retrieval Engine that can carry out information retrieval using noisy documents where documents are generated via transcription that can introduce errors. The work entails initial research into existing methods of robust information retrieval and solutions proposed for similar problems, preparing a research report from the findings, identifying the requirements, and proposing a solution application that can carry out information retrieval with noisy data, and finally, development and deployment of the proposed system with necessary integrations.

Main Features

Machine Learning

We tested classic and Learn-To-Rank search algorithms for noisy documents and implemented them in Elasticsearch

Web-Application

Users can easily create an account, log-in and search for learning material using our search engine (Language Model with Dirichlet Smoothing)

Project Video

Team Members

Thatchawin Leelewat

Full Stack developer, Devops

Vincent Lefeuve

Back-end developer, Information retrieval

Tim Widmayer

Team Manager, Report Editor, Developer

Gantt Chart