A simple Bayesian modelling approach to event extraction from Twitter

Deyu Zhou, Liangyu Chen, Yulan He

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

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%.

Original languageEnglish
Title of host publicationThe 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
Publication statusPublished - 2014
Event52nd annual meeting of the Association for Computational Linguistics - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014

Meeting

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

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twitter
event
social media
Bayesian Modeling
proliferation
Social Media

Cite this

Zhou, D., Chen, L., & He, Y. (2014). A simple Bayesian modelling approach to event extraction from Twitter. In The 52nd Annual Meeting of the Association for Computational Linguistics (ACL) (Vol. 2, pp. 700-705). Association for Computational Linguistics.
Zhou, Deyu ; Chen, Liangyu ; He, Yulan. / A simple Bayesian modelling approach to event extraction from Twitter. The 52nd Annual Meeting of the Association for Computational Linguistics (ACL). Vol. 2 Association for Computational Linguistics, 2014. pp. 700-705
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title = "A simple Bayesian modelling approach to event extraction from Twitter",
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{\%}.",
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Zhou, D, Chen, L & He, Y 2014, A simple Bayesian modelling approach to event extraction from Twitter. in The 52nd Annual Meeting of the Association for Computational Linguistics (ACL). vol. 2, Association for Computational Linguistics, pp. 700-705, 52nd annual meeting of the Association for Computational Linguistics, Baltimore, MD, United States, 22/06/14.

A simple Bayesian modelling approach to event extraction from Twitter. / Zhou, Deyu; Chen, Liangyu; He, Yulan.

The 52nd Annual Meeting of the Association for Computational Linguistics (ACL). Vol. 2 Association for Computational Linguistics, 2014. p. 700-705.

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

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AB - 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%.

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Zhou D, Chen L, He Y. A simple Bayesian modelling approach to event extraction from Twitter. In The 52nd Annual Meeting of the Association for Computational Linguistics (ACL). Vol. 2. Association for Computational Linguistics. 2014. p. 700-705