Jointly event extraction and visualization on Twitter via probabilistic modelling

Deyu Zhou, Tianmeng Gao, Yulan He

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

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.
Original languageEnglish
Title of host publicationThe 54th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationproceedings of the conference
PublisherAssociation for Computational Linguistics
Pages269-278
Number of pages10
Volume1
ISBN (Electronic)978-1-5108-2758-5
Publication statusPublished - 15 Aug 2016
Event54th Annual Meeting of the Association for Computational Linguistics - Humboldt University, Berlin, Germany
Duration: 7 Aug 201612 Aug 2016

Meeting

Meeting54th Annual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2016
CountryGermany
CityBerlin
Period7/08/1612/08/16

Bibliographical note

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