NLP - Embedding Dimensionality Reduction Approaches

Project repository of group 14, nlp course @ university of bonn

Members

Arwah Jawwad
Behnam Fanitabasi
Behzad Shomali
Mahnaz Mirhaj
Sven Knauer

Project Outline

project_outline

Dataset

We use three different datasets: Aspect Sentiment Classification Dataset - CheckThat Lab 2023 - Subjectivity Detection - FEVER
The embeddings are calculated using SBERT.

Approach 1

Variational Autoencoder

Architecture of Variational Autoencoder (source)

Approach 2

Traditional Dimensionality Reduction techniques such as PCA, LDA, and t-SNE.

Approach 3

Inspired by: A novel approach for dimension reduction using word embedding: An enhanced text classification approach Link Singh et al., International Journal of Information Management Data Insights Volume 2, Issue 1, April 2022

Results and Report

Experiments are done with ML classifiers such as SVM, MLP, and Random Forest on the three datasets, Naive Bayes, and KNN. F1-Scores, Time Inference, and Memory Consumption Plots: Result
Report: Final report