This repository contains code for our project work as part of the E0-334 Deep Learning for Natural Language Processing course at IISc, Bengaluru. We had proposed a graph-based model for text classification.
Cluster labelling was done by using the power of wikipedia search
Code for ICASSP paper. FEMDA: Robust classification with Flexible Discriminant Analysis in heterogeneous data. Flexible EM-Inspired Discriminant Analysis is a robust supervised classification algorithm
Project work as part of the E0-334 Deep Learning for Natural Language Processing course at IISc, Bengaluru. We had proposed a graph-based model for text classification.
Naive Bayes classifier and boolean retrieval done on the 20Newsgroups dataset that has been written from scratch. Extremely lightweight and produces decent results. Also currently working on classification using word embeddings.
Assignment 2 – Dimensionality reduction and text classification: converted news text into a machine readable representation, reduced the dimensions of the text representation and trained classifiers to decide which of 20 news groups a sample belongs to.
Experimentation with novelty detection
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