A simple Bayesian modelling approach to event extraction from Twitter

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

View graph of relations Save citation


Research units


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%.

Request a copy

Request a copy


Publication date2014
Publication titleThe 52nd Annual Meeting of the Association for Computational Linguistics (ACL)
PublisherAssociation for Computational Linguistics
Number of pages6
ISBN (Print)978-1-937284-73-2
Original languageEnglish
Event52nd annual meeting of the Association for Computational Linguistics - Baltimore, MD, United States
Duration: 22 Jun 201427 Jun 2014


Meeting52nd annual meeting of the Association for Computational Linguistics
Abbreviated titleACL 2014
CountryUnited States
CityBaltimore, MD

Employable Graduates; Exploitable Research

Copy the text from this field...