文件分群係根據一群文件的內容自動將其組織成有意義的類別。現有的文件分群技術大多只處理單語文件，也就是所有文件只以單一種語言所寫成。然而隨著國際化的趨勢以及網際網路科技的發展，組織或個人常常會產生、獲取進而儲存不同語言之文件，也因此產生了對多語文件自動分群技術的需要。此技術的重要性及需要性激發了本研究的動機，於是我們設計了一個基於潛藏語意分析之多語文件自動分群技術。我們的實證評估以cluster recall與cluster precision來衡量分群效果，結果顯示所提出之多語文件分群技術達到令人滿意的效果。

Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a Latent Semantic Indexing (LSI) based MLDC technique. Our empirical evaluation results show that the proposed LSI-based multilingual document clustering technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision.

Chapter 1 Introduction 1
1.1 Background 1
1.2 Research Motivation and Objectives 2
1.3 Organization of the Thesis 3
Chapter 2 Literature Review 5
2.1 Non-LSI-based Monolingual Document Clustering 5
2.2 Latent Semantic Indexing 9
2.3 Other LSI-based Text Processing Techniques 11
2.3.1 LSI-based Cross-Lingual Information Retrieval 11
2.3.2 LSI-based Document Clustering 13
2.4 Related Works on Multilingual Document Clustering 13
Chapter 3 Design of LSI-based Multilingual Document Clustering (MLDC) Technique 15
3.1 Multilingual Semantic Structure Analysis Phase 16
3.2 Document Folding-in Phase 20
3.3 Document Clustering Phase 22
Chapter 4 Empirical Evaluation 24
4.1 Evaluation Design 24
4.1.1 Data Collection 24
4.1.2 Evaluation Procedure 25
4.1.3 Evaluation Criteria 26
4.2 Evaluation Results 27
4.2.1 Effects of Number of Dimensions and Document Representation Scheme 27
4.2.2 Performance Analysis of Clustering Monolingual and Cross-Lingual Documents Using MLDC 30
4.2.3 Effects of Dimension Selection 33
4.2.4 Performance Analysis of Clustering English or Chinese Documents Using MLDC 36
Chapter 5 Conclusion and Future Research Directions 40
References 42

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