Topic Detection and Tracking

Inhaltsverzeichnis

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 3 Detection.
• 5 Acknowledgements.
• 9 Signal Boosting for Translingual Topic Tracking.
• 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.
• 2 Basic System.
• 3 Tracking.
• 4 Cluster Detection.
• 6 Link Detection.
• 7 Bounds on Effectiveness.
• 8 Automatic Timeline Generation.
• 9 Conclusions.
• 11 Towards a “Universal Dictionary” for Multi-Language IR Applications.
• 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.
• 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.