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Introduction:

In recent years, Natural Language Processing (NLP) systems have made significant advancements in understanding and generating human language. However, one major limitation of current NLP systems is their lack of commonsense reasoning abilities. Commonsense reasoning, the ability to make inferences about everyday situations using background knowledge, is a crucial component of human intelligence that is often taken for granted. Integrating commonsense reasoning with NLP systems has the potential to significantly improve their performance in various tasks such as text understanding, question answering, and dialogue generation.

This thesis aims to investigate the integration of commonsense reasoning with NLP systems. By leveraging the vast amount of commonsense knowledge available in various sources such as knowledge bases, ontologies, and large-scale corpora, we aim to enhance the capabilities of NLP systems in handling real-world language understanding tasks.

Table of Contents:

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 Overview of NLP Systems
2.2 Commonsense Reasoning in AI
2.3 Integration of Commonsense Reasoning with NLP
2.4 Knowledge Representation for Commonsense Reasoning
2.5 Approaches to Incorporating Commonsense Knowledge in NLP Systems
2.6 Evaluation Metrics for Commonsense Reasoning in NLP
2.7 Challenges and Limitations in Integrating Commonsense Reasoning with NLP
2.8 State-of-the-Art Systems in Commonsense Reasoning and NLP Integration
2.9 Future Directions in Commonsense Reasoning and NLP

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Knowledge Base Integration
3.4 Machine Learning Models for Commonsense Reasoning
3.5 Evaluation Framework
3.6 Experimental Setup
3.7 Performance Metrics
3.8 Validation Process

Chapter 4: System Implementation
4.1 Implementation Details
4.2 Integration of Commonsense Reasoning Components
4.3 Training and Testing Procedures
4.4 Performance Analysis
4.5 Results Interpretation
4.6 Error Analysis
4.7 Optimization Techniques
4.8 Scalability and Efficiency

Chapter 5: Conclusion and Summary
5.1 Recap of Study Objectives
5.2 Discussion of Findings
5.3 Contributions to the Field
5.4 Implications for NLP Research
5.5 Future Work and Recommendations
5.6 Conclusion

This thesis will provide a comprehensive overview of the current state of commonsense reasoning in NLP systems and propose novel approaches for integrating commonsense knowledge into NLP models. The findings of this research will contribute to the advancement of NLP technology and pave the way for more intelligent and context-aware language processing systems.

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