A simple Bayesian modelling approach to event extraction from Twitter

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Abstract

With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events. Therefore, it is crucial to extract events from the social streams such as tweets. However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy. In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media. LEM is fully unsupervised and does not require annotated data for training. We evaluate LEM on a Twitter corpus. Experimental results show that the proposed model achieves 83% in F-measure, and outperforms the state-of-the-art baseline by over 7%.© 2014 Association for Computational Linguistics.

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Details

Publication date2014
Publication titleThe 52nd Annual Meeting of the Association for Computational Linguistics (ACL)
PublisherAssociation for Computational Linguistics
Pages700-705
Number of pages6
Volume2
ISBN (Print)978-1-937284-73-2
Original languageEnglish
Event52nd annual meeting of the Association for Computational Linguistics - Baltimore, MD, United States

Meeting

Meeting52nd annual meeting of the Association for Computational Linguistics
Abbreviated titleACL 2014
CountryUnited States
CityBaltimore, MD
Period22/06/1427/06/14

Research outputs

Employable Graduates; Exploitable Research

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