Paper: A Novel Burst-based Text Representation Model for Scalable Event Detection

ACL ID P12-2009
Title A Novel Burst-based Text Representation Model for Scalable Event Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

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.