@inproceedings{ge-etal-2016-event,
title = "Event Detection with Burst Information Networks",
author = "Ge, Tao and
Cui, Lei and
Chang, Baobao and
Sui, Zhifang and
Zhou, Ming",
editor = "Matsumoto, Yuji and
Prasad, Rashmi",
booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
month = dec,
year = "2016",
address = "Osaka, Japan",
publisher = "The COLING 2016 Organizing Committee",
url = "https://aclanthology.org/C16-1309/",
pages = "3276--3286",
abstract = "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. 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."
}
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%0 Conference Proceedings
%T Event Detection with Burst Information Networks
%A Ge, Tao
%A Cui, Lei
%A Chang, Baobao
%A Sui, Zhifang
%A Zhou, Ming
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F ge-etal-2016-event
%X 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. 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.
%U https://aclanthology.org/C16-1309/
%P 3276-3286
Markdown (Informal)
[Event Detection with Burst Information Networks](https://aclanthology.org/C16-1309/) (Ge et al., COLING 2016)
ACL
- Tao Ge, Lei Cui, Baobao Chang, Zhifang Sui, and Ming Zhou. 2016. Event Detection with Burst Information Networks. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3276–3286, Osaka, Japan. The COLING 2016 Organizing Committee.