Enhancing Natural Language Processing Models for Contextual Understanding in Low-Resource Languages+

Pesaru, Abhijith Reddy (2023) Enhancing Natural Language Processing Models for Contextual Understanding in Low-Resource Languages+. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (1MB) | Preview
Preview	PDF (Configuration Manual) Download (1MB) | Preview

Abstract

This paper is focused on the improvement of language models that have limited linguistic resources, often referred to as ”low-resource languages”. The goal is to make Natural Language Processing models more effective and accurate in understanding and processing these languages. Natural language processing (NLP) is one of the most advanced fields in the area of Machine Learning (ML) which will help people interact with technology in their own language through various sources like chatbots which will empower people to take suggestions right from day to day to life routines to making future plans in their own language from a pre-trained model. But when it comes to low-resource languages like Telugu which doesn’t have many linguistic online resources it makes NLP challenging for such kinds of languages. This Project aims at bridging this gap by using algorithms like Bert, Robert, LSTM along with stacking ensemble models for enhancing Telugu NLP. Through a comprehensive analysis of these techniques, we aim to improve the contextual understanding and overall performance of NLP models for the Telugu language.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Chikkankod, Arjun UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	20 May 2025 14:21
Last Modified:	20 May 2025 14:21
URI:	https://norma.ncirl.ie/id/eprint/7590

Actions (login required)

View Item