Project Abstract

With the emergence of deep learning technologies, natural language processing (NLP) has undergone a paradigm shift. This preliminary project report explores the intersection between NLP and deep learning, aiming to utilize the power of neural networks to solve a range of language comprehension and generation tasks. This report provides a comprehensive overview of project objectives, methods, preliminary findings, and future directions.Since the 2010s, with the rise of deep learning technology, the NLP field has ushered in a new revolution. Deep learning models such as Recurrent Neural Networks (RNN), Long Short Term Memory Networks (LSTM), and Transformers have been widely used in NLP tasks and have achieved breakthrough results, such as machine translation, text generation, and sentiment analysis.In order to address major NLP difficulties, this study will investigate how natural language processing (NLP) and deep learning may work together. Additionally, new techniques and technologies will be suggested in order to advance language In conclusion, this work highlights the value of fusing deep learning methods with natural language processing (NLP) and highlights the implications of this combination for the NLP discipline and other application domains. These results contribute to the advancement of technology and set the stage for further study and creativity in this fascinating and quickly evolving subject.

Keywords: natural language processing, deep learning,

 Conference Details

Session: Presentation Stream 17 at Presentation Slot 1

Location: GH011 at Wednesday 8th 09:00 – 12:30

Markers: Arnold Beckmann, Alex Warren (GTA)

Course: MSc Advanced Computer Science, Masters PG

Future Plans: I’m looking for work