像是新聞文件、個人郵件、 blog, 或是某個產品的客戶服務紀錄，隨時間一天天累積，這樣子具有時間順序性的文件，無所不在。一個這樣子的文件，描述著某個事件主題；事件是發生在某個時間地點，例如倫敦地鐵爆炸案、或是針對某種特定目的，例如討論清大交大整併案的會議紀錄。事件隨著時間的演進而有階段性的發展，本文主旨在這麼大量隨時間累積的文件當中，找到這樣子事件階段的結構。傳統技術用文件分群，而本文導入了時間的概念，提出一個temporal-IDF並且在實證研究中証實比傳統技術能得到更好的分群效果。

Recent advances in information and networking technologies have contributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns in this study) in sequences of documents. The discovery of such evolution patterns can be applied in such domains as knowledge management and used to facilitate existing document management and retrieval techniques (e.g., event tracking). Specifically, we propose and design an evolution pattern (EP) discovery technique for mining evolution patterns from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating event tracking. Measured by miss and false alarm rates, the evolution-pattern supported event-tracking (EPET) technique exhibits better tracking effectiveness than a traditional event-tracking technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of evolution patterns in supporting event tracking and suggests that the proposed EP technique could effectively discover event episodes and evolution patterns in sequences of documents.

1 Introduction 1
1.1 Background and Motivation 1
1.2 Overview solution 6
2 Problem Definition, Literature Review 9
2.1 Preliminaries 10
2.2 Event-Episode-Story Structure in Document Sequences 13
2.3 Event Episode Identification 15
2.4 Research Challenges 16
2.5 Literature Review 19
2.6 Other Techniques related to Organization of Event-based Document Stream 23
3 Design of Temporal-based Event Episode Identification Technique 27
3.1 Problem Analysis: Document Clustering Utilizing Temporal Characteristics 28
3.1.1 MODELING EVENT EPISODE IDENTIFICATION AS A DOCUMENT CLUSTERING PROBLEM 28
3.1.3 Selecting Representative Features, Contributing to Similarity Meaures 31
3.2 TF

[ACD+98] Allan, J., Carbonell, J., Doddington, G., Yamron, J., and Yang, Y., “Topic detection and tracking pilot study: Final report,” Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, February 1998, pp.194–218. (Cite in: Problem Definition)
[AGF+05] Allan, J., Harding, S., Fisher, D., Bolivar, A., Guzman-Lara, S., and Amstutz, P., "Taking Topic Detection From Evaluation to Practice," CD Proceedings of the Thirty-Eighth Annual Hawaii International Conference on System Sciences (HICSS), Big Island, Hawaii, January 2005, pp.101a–101a. (Cite in: Related Work)
[ALS02] Allen, J., Lavrenko, V., and Swan, R., “Explorations within topic tracking and detection,” Chapter 10 in Topic Detection and Tracking: Event-based Information Organization, James Allan (Ed.), Kluwer Academic Publishers, 2002, pp.197–224. (Cite in: Section 5.3)
[APL98] Allan, J., Papka, R., and Lavrenko, V., “On-line New Event Detection and Tracking,” Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’98), Melbourne, Australia, August 1998, pp.37–45. (Cite in: Problem Definition)
[CSG+02] Cieri, C., Strassel, S., Graff, D., Martey, N., Rennert, K., and Liberman, M., “Corpora for topic detection and tracking,” Chapter 3 in Topic Detection and Tracking: Event-based Information Organization, James Allan (Ed.), Kluwer Academic Publishers, 2002, pp.33–66. (Cite in: Problem Definition)
[DJ04] Diaz, F. and Jones, R., “Using temporal profiles of queries for precision prediction,” Proceedings of the 27th annual international conference on Research and development in information retrieval (SIGIR’04), Sheffield, United Kingdom, 2004, pp.18-24.
[GDH03] Gabrilovich, E., Dumais, S., and Horvitz, E., “Newsjunkie: providing personalized newsfeeds via analysis of information novelty,” Proceedings of the 13th international conference on World Wide Web (WWW’04), New York, NY, USA, May 2003, pp.482-489.
[HGM00] Hatzivassiloglou, V., Gravano, L., and Maganti, A., “An investigation of linguistic features and clustering algorithms for topical document clustering,” Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR’00), Athens, Greece, 2000, pp.224 – 231.
[KG02] Kab