近來電腦及網路科技的快速發展促成了全球網路的連結，也使得線上文件快速地成長及累積。這些在網路上或組織內所累積下來的文件可能含有許多組織競爭所需的知識，有效的文件管理(Document Management)技術(包括資訊檢索(Information Retrieval)、資訊過濾(Information Filtering)、文字探勘(Text Mining)等)可協助組織有效的運用這些文件。然而，文件管理研究面臨一項挑戰性的議題，即所謂的字詞使用差異(Word Mismatch)。目前字詞使用差異的研究主要是在資訊檢索的研究領域，並以字詞擴展(Term Expansion)的技術來解決這個問題，然而，在文件探勘的文獻中，這個問題卻極少被處理與解決。因此，本論文旨在對文件探勘技術中字詞擴展之使用進行研究，並特別以文件分類(Text Categorization)、文件分群(Document Clustering)以及事件偵測(Event Detection)這三類文件探勘技術為研究對象，發展這三類技術所需之字詞擴展技術。根據實證評估的結果，當使用相關係數(Correlation Coefficient)作為特徵選擇(Feature Selection)方式時，字詞擴展技術增加了的文件分類之效能。在文件分群方面，使用字詞擴展之文件分群技術並未改善分群之效能，但在Specificity的衡量上，使用字詞擴展技術的結果普遍明顯地優於傳統文件分群技術。最後，使用字詞擴展來協助事件偵測則導致了偵測效果的降低。

Recent advances in computer and network technologies have contributed significantly to global connectivity and stimulated the amount of online textual document to grow extremely rapidly. The rapid accumulation of textual documents on the Web or within an organization requires effective document management techniques, covering from information retrieval, information filtering and text mining. The word mismatch problem represents a challenging issue to be addressed by the document management research. Word mismatch has been extensively investigated in information retrieval (IR) research by the use of term expansion (or specifically query expansion). However, a review of text mining literature suggests that the word mismatch problem has seldom been addressed by text mining techniques. Thus, this thesis aims at investigating the use of term expansion on some text mining techniques, specifically including text categorization, document clustering and event detection. Accordingly, we developed term expansion extensions to these three text mining techniques. The empirical evaluation results showed that term expansion increased the categorization effectiveness when the correlation coefficient feature selection was employed. With respect to document clustering, techniques extended with term expansion achieved comparable clustering effectiveness to existing techniques and showed its superiority in improving clustering specificity measure. Finally, the use of term expansion for supporting event detection has degraded the detection effectiveness as compared to the traditional event detection technique.

CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 RESEARCH MOTIVATION AND OBJECTIVES 1
1.3 ORGANIZATION OF THE THESIS 3
CHAPTER 2 LITERATURE REVIEW 5
2.1 TERM ASSOCIATION CONSTRUCTION 5
2.2 TEXT CATEGORIZATION 9
2.3 DOCUMENT CLUSTERING 14
2.4 EVENT DETECTION 16
CHAPTER 3 TEXT MINING TECHNIQUES WITH TERM EXPANSION 20
3.1 TERM ASSOCIATION CONSTRUCTION PROCESS 20
3.2 TEXT CATEGORIZATION WITH TERM EXPANSION 22
3.3 DOCUMENT CLUSTERING WITH TERM EXPANSION 24
3.4 EVENT DETECTION WITH TERM EXPANSION 26
CHAPTER 4 EMPIRICAL EVALUATION FOR TEXT CATEGORIZATION WITH TERM EXPANSION 29
4.1 EVALUATION DESIGN 29
4.1.1 Data collection 29
4.1.2 Evaluation Criteria 31
4.1.3 Evaluation Procedure 31
4.2 EVALUATION RESULT 32
4.2.1 Effects of Number of Features on Categorization Effectiveness 32
4.2.2 Effects of Induction Method, Feature Selection Method and Representation Scheme on Categorization Effectiveness 36
4.2.3 Comparative Evaluation of Text Categorization Techniques 38
CHAPTER 5 EMPIRICAL EVALUATION FOR TERM EXPANSION EMBEDDED DOCUMENT CLUSTERING 42
5.1 EVALUATION DESIGN 42
5.1.1 Evaluation Criteria 42
5.1.2 Evaluation Procedure 44
5.2 EVALUATION RESULT 45
5.2.1 Comparative Evaluation of Document Clustering Techniques 45
CHAPTER 6 EMPIRICAL EVALUATION FOR EVENT DETECTION WITH TERM EXPANSION 50
6.1 EVALUATION DESIGN 50
6.1.1 Data Collection 50
6.1.2 Evaluation Criteria 51
6.1.3 Performance Benchmark 51
6.1.4 Evaluation Procedure 52
6.2 EVALUATION RESULT 52
6.2.1 Parameter Tuning 52
6.2.2 Comparative Evaluation of Event Detection Techniques 56
CHAPTER 7 CONCLUSION AND FUTURE RESEARCH DIRECTIONS 57
REFERENCES 59

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