Mining retrospective events from text streams has been an important research topic. Classic text representation model (i.e., vector space model) cannot model temporal aspects of doc- uments. To address it, we proposed a novel burst-based text representation model, de- noted as BurstVSM. BurstVSM corresponds dimensions to bursty features instead of terms, which can capture semantic and temporal in- formation. Meanwhile, it significantly reduces the number of non-zero entries in the repre- sentation. We test it via scalable event de- tection, and experiments in a 10-year news archive show that our methods are both effec- tive and efficient.

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