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

8 years 6 months ago
A Novel Burst-based Text Representation Model for Scalable Event Detection
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 documents. To address it, we proposed a novel burst-based text representation model, denoted as BurstVSM. BurstVSM corresponds dimensions to bursty features instead of terms, which can capture semantic and temporal information. Meanwhile, it significantly reduces the number of non-zero entries in the representation. We test it via scalable event detection, and experiments in a 10-year news archive show that our methods are both effective and efficient.
Xin Zhao, Rishan Chen, Kai Fan, Hongfei Yan, Xiaom
Added 29 Sep 2012
Updated 29 Sep 2012
Type Journal
Year 2012
Where ACL
Authors Xin Zhao, Rishan Chen, Kai Fan, Hongfei Yan, Xiaoming Li
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