隨著網際網路的蓬勃發展，許多來自不同國度有趣及具有創造力的應用逐漸被開發出來，電子商務也愈來愈普及。因應此潮流，在現代全球化的商業環境中，大量的資訊在組織與個人間不斷地被共享與交換，其中相當大部分的資訊是以文字形態表達，並以類別分類的方式來作管理，故如何發展一個實用並有效的技術來協助解決跨語言文件類別整合問題便成為一個不得不面對的重要課題。實際上現存的許多類別整合技術已被用於解決單語文件類別整合的問題，但並沒有相關技術或研究涉足到跨語言文件類別整合的領域。因此，本研究結合跨語言文件分類技術中所使用的跨語機制，加上現存的單語文件類別整合技術─加強式貝式分類器 (Enhanced Naïve Bayes)，提出一個跨語言文件類別整合的解決方案。實驗結果顯示，本研究所提出之跨語言文件類別整合技術具有可行性並可達到優異的效能。

With the emergence of the Internet, many innovative and interesting applications from different countries have been stimulated and e-commerce is also getting more and more pervasive. Under this scenario, tremendous amount of information expressed in different languages are exchanged and shared by not only organizations but also individuals in the modern global environment. A large proportion of information is typically formatted and available as textual documents and managed by using categories. Consequently, the development of a practical and effective technique to deal with the problem of cross-lingual category integration (CLCI) becomes a very essential and important issue. Several category integration techniques have been proposed, but all of them deal with category integration involving only monolingual documents. In response, in this study, we combine the existing cross-lingual text categorization techniques with an existing monolingual category integration technique (specifically, Enhanced Naive Bayes) and proposed a CLCI solution to address cross-lingual category integration. Our empirical evaluation results show that our proposed CLCI technique demonstrates its feasibility and superior effectiveness.

Chapter 1 Introduction
1.1 Background
1.2 Research Motivation and Objective
1.3 Organization of the Thesis
Chapter 2 Literature Review
2.1 Monolingual Category Integration Techniques
2.1.1 Enhanced Naive Bayes (ENB) Technique
2.1.2 Cluster Shrinkage Approach
2.1.3 Co-Bootstrapping Technique
2.1.4 Clustering-based Category Integration (CCI) Technique
2.2 Cross-Lingual Text Categorization
2.2.1 Prediction-corpus Translation-based CLTC Approach
2.2.2 Training-corpus Translation-based CLTC Approach
Chapter 3 Design of Cross-Lingual Category Integration (CLCI) Technique
3.1 Cross-Lingual Thesaurus Construction
3.2 Translation Approaches
3.2.1 Source-catalog Translation
3.2.2 Master-catalog Translation
3.3 Category Integration
Chapter 4 Empirical Evaluation
4.1 Data Collection
4.2 Evaluation Design
4.2.1 Dimension of Evaluations and Creation of Synthetic Catalogs
4.2.2 Evaluation Procedure and Criteria
4.3 Evaluation Results
4.3.1 Parameter Tuning Experiments
4.3.2 Comparative Evaluation
4.3.3 Effects of Data Size on Integration Accuracy
Chapter 5 Conclusion and Future Research Directions
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