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Introduction

Question Answering is a fundamental task in natural language processing that involves understanding a question posed in natural language and providing a correct answer based on the information available. Over the years, various question answering systems have been developed using different techniques. One of the breakthroughs in recent years is the introduction of BERT (Bidirectional Encoder Representations from Transformers) models, which have shown state-of-the-art performance on various natural language processing tasks.

This thesis aims to develop a question answering system using BERT models to improve the accuracy and efficiency of answering questions in natural language. The system will be designed to extract relevant information from a given text and generate accurate answers to questions posed by users.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to Question Answering Systems
2.2 Overview of BERT Models
2.3 Previous Work on Question Answering with BERT
2.4 Evaluation Metrics for Question Answering Systems
2.5 Comparison of BERT Models with Other Techniques
2.6 Fine-Tuning BERT for Question Answering
2.7 Challenges in Question Answering with BERT
2.8 Applications of BERT in Natural Language Processing
2.9 Future Directions in Question Answering Research
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 Introduction
3.2 Data Collection and Preprocessing
3.3 BERT Model Selection and Fine-Tuning
3.4 Training the Question Answering System
3.5 Evaluation Metrics for Performance
3.6 System Architecture
3.7 Implementation Details
3.8 Testing and Validation
3.9 Discussion on Results
3.10 Summary of System Design and Methodology

Chapter 4: System Implementation
4.1 Introduction
4.2 Building the Question Answering System
4.3 Integration of BERT Models
4.4 User Interface Design
4.5 System Testing and Performance Evaluation
4.6 Improvements and Enhancements
4.7 Challenges and Solutions
4.8 Impact of System Implementation
4.9 Summary of System Implementation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Research and Practice
5.4 Limitations and Future Directions
5.5 Conclusion

Thesis Overview

The goal of this thesis is to develop a question answering system using BERT models, a cutting-edge technology in natural language processing. Chapter 1 provides an introduction to the study, including the background, problem statement, objectives, limitations, scope, significance, structure, and definition of terms. Chapter 2 presents a comprehensive literature review on question answering systems and BERT models, covering previous work, evaluation metrics, challenges, applications, and future directions.

Chapter 3 details the system design and methodology, including data collection, preprocessing, model selection, fine-tuning, training, evaluation metrics, architecture, implementation details, testing, and validation. Chapter 4 focuses on system implementation, discussing building the system, integrating BERT models, user interface design, testing, improvements, challenges, solutions, and impact.

Finally, Chapter 5 concludes the thesis with a summary of findings, contributions, implications, limitations, future directions, and a conclusion. The thesis aims to contribute to the field of natural language processing by developing an efficient question answering system using BERT models.

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