Jointly event extraction and visualization on Twitter via probabilistic modelling


Event extraction from texts aims to detect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to jointly extract and visualize events from tweets where both tasks benefit from each other. We model each event as a joint distribution over named entities, a date, a location and event-related keywords. Moreover, both tweets and event instances are associated with coordinates in the visualization space. The manifold assumption that the intrinsic geometry of tweets is a low-rank, non-linear manifold within the high-dimensional space is incorporated into the learning framework using a regularization. Experimental results show that the proposed approach can effectively deal with both event extraction and visualization and performs remarkably better than both the state-of-the-art event extraction method and a pipeline approach for event extraction and visualization.

Divisions: Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: -
Event Title: 54th Annual Meeting of the Association for Computational Linguistics
Event Type: Other
Event Location: Humboldt University
Event Dates: 2016-08-07 - 2016-08-12
Uncontrolled Keywords: Language and Linguistics,Linguistics and Language
ISBN: 978-1-5108-2758-5
Full Text Link: http://www.aclw ... 16/P16-1026.pdf
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2016-08-15
Accepted Date: 2016-08-01
Authors: Zhou, Deyu
Gao, Tianmeng
He, Yulan ( 0000-0003-3948-5845)

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