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Introduction:

Word sense disambiguation (WSD) is a crucial task in the field of lexical semantics, with the objective of determining the correct sense of a word within a given context. The ambiguity of natural language poses a significant challenge in understanding and processing text, leading to errors in applications such as machine translation, information retrieval, and text mining. WSD plays a vital role in improving the accuracy of these applications by disambiguating the various senses of ambiguous words.

This thesis aims to explore and analyze various methods and techniques for performing WSD, with a focus on leveraging computational linguistics and artificial intelligence. The study will investigate the latest advancements in WSD algorithms and evaluate their effectiveness in disambiguating word senses in different contexts.

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 Word Sense Disambiguation
2.2 Traditional WSD Approaches
2.3 Knowledge-Based Approaches
2.4 Supervised Machine Learning Approaches
2.5 Unsupervised Machine Learning Approaches
2.6 Hybrid Approaches
2.7 Evaluation Metrics for WSD
2.8 Challenges and Issues in WSD
2.9 Recent Developments in WSD
2.10 Future Directions in WSD Research

Chapter 3: System Design and Methodology
3.1 Data Collection and Preprocessing
3.2 Feature Selection and Extraction
3.3 Algorithm Selection
3.4 Training and Testing Data
3.5 Model Evaluation
3.6 Parameter Tuning
3.7 Cross-Validation
3.8 Performance Analysis

Chapter 4: System Implementation
4.1 Development Environment
4.2 System Architecture
4.3 Implementation Details
4.4 Integration with Existing Systems
4.5 Testing and Validation
4.6 Performance Optimization
4.7 User Interface Design
4.8 System Deployment

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

Thesis Overview:

Word sense disambiguation (WSD) is a fundamental problem in computational linguistics, aiming to determine the correct meaning of an ambiguous word in a given context. The ambiguity of natural language poses a challenge in understanding and processing text, leading to errors in various applications. This thesis focuses on exploring and evaluating different methods and techniques for performing WSD, with an emphasis on computational linguistics and artificial intelligence.

Chapter 1 provides an introduction to the study, outlining the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter 2 conducts a comprehensive literature review on WSD, discussing traditional approaches, knowledge-based methods, supervised and unsupervised machine learning techniques, evaluation metrics, challenges, recent developments, and future research directions.

Chapter 3 details the system design and methodology for WSD, covering data collection, preprocessing, feature selection, algorithm selection, training, testing, evaluation, parameter tuning, and performance analysis. Chapter 4 elaborates on the system implementation, including the development environment, architecture, details, integration, testing, optimization, user interface design, and deployment. Finally, Chapter 5 offers a conclusion and summary of the study, highlighting the findings, contributions, implications, limitations, recommendations, and conclusion.

Overall, this thesis aims to contribute to the advancement of WSD research and provide insights for improving the accuracy of applications relying on lexical semantics.

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