A study of retrospective and on-line event detection
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
On-line new event detection and tracking
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Improving text categorization methods for event tracking
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
First story detection in TDT is hard
Proceedings of the ninth international conference on Information and knowledge management
Unsupervised and supervised clustering for topic tracking
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Topic Detection and Tracking: Event-Based Information Organization
Topic Detection and Tracking: Event-Based Information Organization
Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
On-line new event detection, clustering, and tracking (information retrieval, internet)
On-line new event detection, clustering, and tracking (information retrieval, internet)
The text retrieval conferences (TRECS)
TIPSTER '98 Proceedings of a workshop on held at Baltimore, Maryland: October 13-15, 1998
Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks
Information Retrieval
Tracking multiple topics for finding interesting articles
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Information Science
Discovering event evolution graphs from news corpora
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Measuring the interestingness of articles in a limited user environment
Information Processing and Management: an International Journal
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The topic tracking task from TDT is a variant of information filtering tasks that focuses on event-based topics in streams of broadcast news. In this study, we compare tracking to another TDT task, detection, which has the goal of partitioning iall arriving news into topics, regardless of whether the topics are of interest to anyone, and even when a new topic appears that had not been previous anticipated. There are clear relationships between the two tasks (under some assumptions, a “perfect” tracking system could “solve” the detection problem), but they are evaluated quite differently. We describe the two tasks and discuss their similarities. We show how viewing detection as a form of multi-topic parallel tracking can illuminate the performance tradeoffs of detection over tracking.