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\title{Deep Learning Approaches for Natural Language Understanding}
\author{Ahmad Rahman\\
\small Graduate Program in Computer Science\\
\small Atlas University}
\date{2024}

\begin{document}

\maketitle

\chapter*{Abstract}
This thesis explores advanced deep learning techniques for natural language understanding tasks. We present novel approaches combining transformer architectures with knowledge graph embeddings to improve semantic comprehension in AI systems.

\tableofcontents

\chapter{Introduction}

\section{Research Background}
Natural Language Understanding (NLU) remains one of the most challenging areas in artificial intelligence. Recent advances in deep learning have significantly improved machine comprehension of human language.

\section{Problem Statement}
Despite progress, current NLU systems struggle with:
\begin{itemize}
    \item Contextual understanding across long documents
    \item Common sense reasoning
    \item Multi-lingual comprehension
    \item Domain adaptation
\end{itemize}

\section{Research Objectives}
This thesis aims to:
\begin{enumerate}
    \item Develop improved transformer architectures for NLU
    \item Integrate knowledge graphs with neural models
    \item Evaluate performance on benchmark datasets
\end{enumerate}

\chapter{Literature Review}

\section{Transformer Models}
The transformer architecture introduced by Vaswani et al. (2017) revolutionized NLP through the attention mechanism:

\begin{equation}
    Attention(Q, K, V) = softmax\left(\frac{QK^T}{\sqrt{d_k}}\right)V
\end{equation}

\section{Pre-trained Language Models}
Models like BERT, GPT, and their variants have achieved state-of-the-art results across NLU tasks.

\chapter{Methodology}

\section{Proposed Architecture}
We propose a hybrid architecture combining:
\begin{itemize}
    \item Multi-head self-attention layers
    \item Graph neural network components
    \item Cross-modal fusion mechanisms
\end{itemize}

\chapter{Experiments and Results}

\section{Datasets}
We evaluate on the following benchmarks:
\begin{itemize}
    \item GLUE benchmark
    \item SQuAD 2.0
    \item Natural Questions
\end{itemize}

\section{Results}
Our model achieves competitive performance across all benchmarks.

\chapter{Conclusion}
This thesis contributes novel approaches to NLU through the integration of transformer models with knowledge graphs.

\end{document}

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Deep Learning Approaches for Natural Language Understanding

Ahmad Rahman, \small Graduate Program in Computer Science, \small Atlas University

2024

Chapter 1: Introduction

Research Background

Problem Statement

Research Objectives

Chapter 5: Literature Review

Transformer Models

Pre-trained Language Models

Chapter 8: Methodology

Proposed Architecture

Chapter 10: Experiments and Results

Datasets

Results

Chapter 13: Conclusion

Contextual understanding across long documents
Common sense reasoning
Multi-lingual comprehension
Domain adaptation

Multi-head self-attention layers
Graph neural network components
Cross-modal fusion mechanisms

GLUE benchmark
SQuAD 2.0
Natural Questions

Develop improved transformer architectures for NLU
Integrate knowledge graphs with neural models
Evaluate performance on benchmark datasets

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