Event Detection with Burst Information Networks - Ge, Tao; Cui, Lei; Chang, Baobao; Sui, Zhifang; Zhou, Ming (COLING 2016, 2016)
Retrospective event detection is an important task for discovering previously unidentiﬁed events in a text stream. In this paper, we propose two fast centroid-aware event detection models based on a novel text stream representation – Burst Information Networks (BINets) for addressing the challenge, following the D2N2K (Data-to-Network-to-Knowledge) paradigm. The BINets are time-aware, efﬁcient and can be easily analyzed for identifying key information (centroids). These advantages allow the BINet-based approaches to achieve the state-of-the-art performance on multiple datasets, demonstrating the efﬁcacy of BINets for the task of event detection.