Improving text categorization methods for event tracking

@inproceedings{Yang2000ImprovingTC,
  title={Improving text categorization methods for event tracking},
  author={Yiming Yang and Tom Ault and Thomas Pierce and Charles W. Lattimer},
  booktitle={SIGIR},
  year={2000}
}
Automated tracking of events from chronologically ordered document streams is a new challenge for statistical text classification. Existing learning techniques must be adapted or improved in order to effectively handle difficult situations where the number of positive training instances per event is extremely small, the majority of training documents are unlabelled, and most of the events have a short duration in time. We adapted several supervised text categorization methods, specifically… CONTINUE READING
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  • All of these methods showed significant improvement (up to 71% reduction in weighted error rates) over the performance of the original kNN algorithm on TDT benchmark collections, making kNN among the top-performing systems in the recent TDT3 official evaluation.

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