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Introduction

Zero-shot learning is a cutting-edge technique in machine learning that aims to address the challenge of classifying unseen classes without any prior training data. In the context of text classification, zero-shot learning involves building models that can accurately classify text documents into categories that were not present in the training data. This thesis explores the potential of zero-shot learning for text classification and investigates its effectiveness in real-world applications.

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 text classification
2.2 Traditional machine learning techniques for text classification
2.3 Zero-shot learning in machine learning
2.4 Zero-shot learning applications in text classification
2.5 Challenges in zero-shot learning for text classification
2.6 Transfer learning in text classification
2.7 Multilingual text classification
2.8 Recent advancements in zero-shot learning
2.9 Evaluation metrics for text classification
2.10 Summary of existing research on zero-shot learning for text classification

Chapter 3: Research Methodology
3.1 Data collection and preprocessing
3.2 Model selection and architecture
3.3 Training and evaluation procedures
3.4 Parameter tuning and optimization
3.5 Cross-validation techniques
3.6 Experimental setup
3.7 Performance evaluation metrics
3.8 Hypothesis testing
3.9 Data analysis techniques

Chapter 4: Discussion of Findings
4.1 Performance comparison of zero-shot learning models
4.2 Impact of different text representations on classification accuracy
4.3 Generalization capabilities of zero-shot learning models
4.4 Interpretability of zero-shot learning models
4.5 Error analysis and model improvements
4.6 Computational efficiency of zero-shot learning models
4.7 Comparison with traditional text classification techniques
4.8 Future research directions in zero-shot learning for text classification

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions of the study
5.3 Implications for future research and applications
5.4 Limitations of the study
5.5 Conclusion and recommendations

Thesis Overview on Zero-shot learning for text classification

Zero-shot learning for text classification is a promising research area in the field of machine learning. This thesis aims to investigate the effectiveness of zero-shot learning techniques in classifying text documents into unseen classes. The study will begin with a comprehensive introduction to zero-shot learning, followed by a review of relevant literature on text classification and zero-shot learning. The research methodology will include data collection, preprocessing, model selection, training procedures, and evaluation metrics. The discussion of findings will cover the performance of zero-shot learning models, the impact of different text representations, generalization capabilities, interpretability, error analysis, and computational efficiency. The thesis will conclude with a summary of key findings, contributions, implications for future research, limitations, and recommendations for further study in this area.

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