The purposeofthis book is to providea recordofthe stateofthe art in Topic Detection and Tracking (TDT) in a single place. Research in TDT has been going on for about five years, and publications related to it are scattered all over the place as technical reports, unpublished manuscripts, or in numerous conference proceedings. The third and fourth in a series of on-going TDT evaluations marked a turning point in the research. As such. it provides an excellent time to pause. review the state of the art. gather lessons learned, and describe the open challenges. This book is a collection oftechnical papers. As such, its primary audience is researchers interested in the the current state of TDT research, researchers who hope to leverage that work sothat theirown efforts can avoid pointlessdu plication and false starts. It might also pointthem in the direction ofinteresting unsolved problems within the area. The book is also of interest to practition ers in fields that are related to TDT--e.g., Information Retrieval. Automatic Speech Recognition. Machine Learning, Information Extraction, and so on. In thosecases,TDTmay provide arich application domain for theirown research, or it might address similarenough problems that some lessons learned can be tweaked slightly to answer-perhaps partiallY-

Inhalt
1 Introduction to Topic Detection and Tracking.- 1 Introduction.- 2 TDT tasks.- 3 History of TDT.- 4 TDT 1999 and TDT.- 5 The Future of TDT.- 2 Topic Detection and Tracking Evaluation Overview.- 1 Introduction.- 2 TDT Definitions: Stories, Events, and Topics.- 3 TDT Corpora.- 4 Evaluation Methodology.- 5 Task Definitions.- 6 Summary.- 3 Corpora for Topic Detection and Tracking.- 1 Introduction.- 2 Overview of TDT Corpus Development.- 3 Collection of Raw Data.- 4 Transcription.- 5 Story Segmentation.- 6 Topic Definition.- 7 Topic Annotation.- 8 Corpus Formats.- 9 Some Properties of the Corpus.- 10 Conclusion.- 4 Probabilistic Approaches to Topic Detection and Tracking.- 1 Introduction.- 2 Core TDT Technologies.- 3 Corpus Processing.- 4 Tracking.- 5 Detection.- 6 Crosslingual TDT.- 7 Conclusions and Future Work.- 8 Acknowledgments.- 5 Multi-strategy Learning for TDT.- 1 Introduction.- 2 Segmentation.- 3 Topic and Event Tracking.- 4 Topic Detection.- 5 First Story Detection.- 6 Story Link Detection.- 7 Multilingual TDT.- 8 Concluding Remarks.- 6 Statistical Models of Topical Content.- 1 Introduction.- 2 Models of Story Generation.- 3 Tracking Systems.- 4 Detection System.- 5 Summary.- 7 Segmentation and Detection at IBM.- 1 Story Segmentation.- 2 Topic Detection.- 3 Acknowledgements.- 8 A Cluster-Based Approach to Broadcast News.- 1 Introduction.- 2 Segmentation.- 3 Detection.- 4 Tracking.- 5 Acknowledgements.- 9 Signal Boosting for Translingual Topic Tracking.- 1 Introduction.- 2 The Signal-to-Noise Perspective.- 3 Topic Tracking System Architecture.- 4 Contrastive Conditions.- 5 Conclusions and Future Work.- 6 Acknowledgments.- 10 Explorations Within Topic Tracking and Detection.- 1 Introduction.- 2 Basic System.- 3 Tracking.- 4 Cluster Detection.- 5 First Story Detection.- 6 Link Detection.- 7 Bounds on Effectiveness.- 8 Automatic Timeline Generation.- 9 Conclusions.- 11 Towards a Universal Dictionary for Multi-Language IR Applications.- 1 Introduction.- 2 Our TDT tracking algorithm.- 3 The Universal Dictionary experiment.- 4 Conclusions and Directions for Future Work.- 12 An NLP & IR Approach to Topic Detection.- 1 Introduction.- 2 General System Framework.- 3 Representation of News Stories and Topics.- 4 Similarity and Interpretation of a Two-threshold Method.- 5 Multilingual Topic Detection.- 6 Development Experiments.- 7 Evaluation.- 8 Discussion.- 9 Concluding Remarks and Future Works.