This repository provides an implementation of topic modelling techniques, namely Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA), specifically designed for analyzing news articles.

Implementation of various Extractive Text Summarization algorithms.

Vector space modeling of MovieLens & IMDB movie data

Walkthrough a toy example of Latent Semantic Analysis

Retrieval of Semantically Relevant Documents using Latent Semantic Analysis

Unsupervised Deep Learning Models Implementation.

Simple chatbot prototype with Latent Semantic Analysis

A web-based Plagiarism Detection System for source code, utilizing Information Retrieval and NLP techniques to identify potential code plagiarism in C, C++, and Python. The tool offers customizable detection levels and supports educators in evaluating programming assignments efficiently.

The script gets a list of words from an excel sheet and will upload them to the following website: http://lsa.colorado.edu/cgi-bin/LSA-matrix.html, "This interface allows you to compare the similarity of multiple texts or terms within a particular LSA space. Each text is compared to all other texts." The results for each subject will be saved in…

Improve Text Segmentation using Latent Semantic Analysis

eea.similarity

Comparison of several dimension reduction methods aiming at the extraction of latent semantic information

Full-text document search engine with latent semantic analysis.

In-memory index-structures for efficient retrieval of multimedia documents based on input queries.

weighted topic modeling

For any given query, an Information Retrieval (IR) system is used to obtain and rank relevant word documents from the data collection of interest. The most basic IR system uses Term Frequency Inverse Document Frequency (TF-IDF) to represent documents and queries as vectors, and then uses measures like cosine similarity to assess the relevance of…

Working towards deliverable 5.3

Repository for the project of Information Retrieval