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.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Xin Zhao, Rishan Chen, Kai Fan, Hongfei Yan, Xiaoming Li
• Keywords: ACL 2012, Computational Linguistics, Text Streams, Vector Space Model, Zero Entries