Personalized information delivery: an analysis of information filtering methods
Communications of the ACM - Special issue on information filtering
Spatial hypertext and the practice of information triage
HYPERTEXT '97 Proceedings of the eighth ACM conference on Hypertext
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
Boosting and Rocchio applied to text filtering
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Language models for financial news recommendation
Proceedings of the ninth international conference on Information and knowledge management
Predicting the effectiveness of Naïve data fusion on the basis of system characteristics
Journal of the American Society for Information Science
Robust Classification for Imprecise Environments
Machine Learning
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Learning Approaches for Detecting and Tracking News Events
IEEE Intelligent Systems
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Probabilistic models for topic detection and tracking
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
Learning trees and rules with set-valued features
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Time-dependent event hierarchy construction
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring the interestingness of articles in a limited user environment
Information Processing and Management: an International Journal
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In many applications, large volumes of time-sensitive textual information require triage: rapid, approximate prioritization for subsequent action. In this paper, we explore the use of prospective indications of the importance of a time-sensitive document, for the purpose of producing better document filtering or ranking. By prospective, we mean importance that could be assessed by actions that occur in the future. For example, a news story may be assessed (retrospectively) as being important, based on events that occurred after the story appeared, such as a stock price plummeting or the issuance of many follow-up stories. If a system could anticipate (prospectively) such occurrences, it could provide a timely indication of importance. Clearly, perfect prescience is impossible. However, sometimes there is sufficient correlation between the content of an information item and the events that occur subsequently. We describe a process for creating and evaluating approximate information-triage procedures that are based on prospective indications. Unlike many information-retrieval applications for which document labeling is a laborious, manual process, for many prospective criteria it is possible to build very large, labeled, training corpora automatically. Such corpora can be used to train text classification procedures that will predict the (prospective) importance of each document. This paper illustrates the process with two case studies, demonstrating the ability to predict whether a news story will be followed by many, very similar news stories, and also whether the stock price of one or more companies associated with a news story will move significantly following the appearance of that story. We conclude by discussing how the comprehensibility of the learned classifiers can be critical to success.}