企業組織必須不斷地對其外部環境進行環境掃瞄以瞭解外部環境的變化，
並求能及時掌握各種環境變動來做出適當的因應決策。隨著組織外在環境
的日漸複雜及資訊科技蓬勃發展所引發的資訊爆炸，已使得組織在進行環
境掃瞄時的負擔更為加重。因此，利用資訊科技來減輕組織進行環境掃瞄
的負擔已勢在必行。事件偵測技術即為協助組織進行環境掃瞄的技術之一
，其能從一連串的新聞稿中藉由比較不同新聞稿之間用字的相似性來偵測
出其所描述的新聞內容為發生過或未發生過的新聞事件。然而、傳統的事
件偵測技術藉由比較新聞稿之間用字差異來進行事件偵測的方式仍有某些
缺點。舉例來說，像是新聞的用字可能會因記者的習慣不同而有差異，因
而導致降低事件偵測的準確性。此外、傳統的事件偵測技術也無法提供事
件分類及後續或相關事件發展追蹤的功能。在本論文中，我們提出了一套
以資訊萃取為基礎的事件偵測技術，其結合了文件分類與資訊萃取的技術
以改善過去事件偵測技術所面臨的限制與問題。在以實際的新聞資料來評
估此新的事件偵測技術時，以0%的錯誤率（miss rate）及9.6%的誤判率（
false alarm rate）獲得了較優於傳統事件偵測技術的結果。

Environmental scanning is an important process, which acquires and uses the information about events, trends, and relationships in an organization's external environment. It permits an organization to adapt to its environment and to develop effective responses to secure or improve their position in the future. Event detection technique that identifies the onset of new events from streams of news stories would facilitate the process of organization's environmental scanning. However, traditional feature-based event detection techniques, which identify whether a news story contains an unseen event by comparing the similarity of words between the news story and past news stories, incur some limitations (e.g., the features shown in news document cannot actually represent the event described in it.). Thus, in this study, we developed an information extraction-based event detection (NEED) technique that combines information extraction and text categorization techniques to address the problems inherent to traditional feature-based event detection techniques. The empirical evaluation results showed that the NEED technique outperformed the traditional feature-based event detection techniques in miss rate and false alarm rate and achieved comparable event association accuracy rate to its counterpart.

Chapter 1. Introduction 1
1.1 Background 1
1.2 Research Motivation and Objectives 2
1.3 Organization of the Thesis 6
Chapter 2. Literature Review 7
2.1 Event Detection 7
2.2 Text Categorization 15
2.2.1 Preprocessing Phase 17
2.2.2 Document Representation Phase 18
2.2.3 Induction Phase 19
2.3 Information Extraction 23
Chapter 3. Development of Information Extraction-based Event Detection (NEED) Technique 29
3.1 Architecture of Information Extraction-based Event Detection (NEED) Technique 29
3.2 Learning and Extraction Subsystem 32
3.3 Detection Subsystem 37
3.4 Complete Algorithm of Information Extraction-based Event Detection (NEED) Technique 41
Chapter 4. Empirical Evaluation 45
4.1 Evaluation Design 45
4.1.1 Data Collection 45
4.1.2 Evaluation Criteria 46
4.1.3 Performance Benchmarks 47
4.1.4 Evaluation Procedure 50
4.2 Evaluation Result 51
4.2.1 Parameter Tuning 51
4.2.2 Comparative Evaluation of Event Detection Techniques 56
4.2.3 Evaluation of Event Association Accuracy 57
Chapter 5. Conclusion and Future Research Directions 59
Appendix A Regular Expression Rule 61
Appendix B Ontology of "Airplane Crash" Event Topic 64
Appendix C Ontology of "Interest Rate Adjustment" Event Topic 65
Appendix D Ontology of "Business Merger" Event Topic 66
Appendix E Ontology of "Business Partnership" Event Topic 67
Appendix F Ontology of "Computer Virus" Event Topic 68
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