Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Introduction to algorithms
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
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
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
An investigation of linguistic features and clustering algorithms for topical document clustering
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Unsupervised and supervised clustering for topic tracking
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Machine Learning
Modern Information Retrieval
Improving realism of topic tracking evaluation
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A critical examination of TDT's cost function
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Topic detection and tracking evaluation overview
Topic detection and tracking
Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Histograms: Capturing Evolving Data Sets
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Simple Semantics in Topic Detection and Tracking
Information Retrieval
A probabilistic model for retrospective news event detection
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
LIPED: HMM-based life profiles for adaptive event detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using Incremental PLSI for Threshold-Resilient Online Event Analysis
IEEE Transactions on Knowledge and Data Engineering
Introduction to Information Retrieval
Introduction to Information Retrieval
Social tags as news event detectors
Journal of Information Science
A significance-driven framework for characterizing and finding evolving patterns of news networks
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
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When an event occurs, it attracts attention of information sources to publish related documents along its lifespan. The task of event detection is to automatically identify events and their related documents from a document stream, which is a set of chronologically ordered documents collected from various information sources. Generally, each event has a distinct activeness development so that its status changes continuously during its lifespan. When an event is active, there are a lot of related documents from various information sources. In contrast when it is inactive, there are very few documents, but they are focused. Previous works on event detection did not consider the characteristics of the event's activeness, and used rigid thresholds for event detection. We propose a concept called life profile, modeled by a hidden Markov model, to model the activeness trends of events. In addition, a general event detection framework, LIPED, which utilizes the learned life profiles and the burst-and-diverse characteristic to adjust the event detection thresholds adaptively, can be incorporated into existing event detection methods. Based on the official TDT corpus and contest rules, the evaluation results show that existing detection methods that incorporate LIPED achieve better performance in the cost and F1 metrics, than without.