An empirical study on uncertainty identification in social media context

Zhongyu Wei, Junwen Chen, Wei Gao, Binyang Li, Lanjun Zhou, Yulan He, Kam-Fai Wong

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Uncertainty text detection is important to many social-media-based applications since more and more users utilize social media platforms (e.g., Twitter, Facebook, etc.) as information source to produce or derive interpretations based on them. However, existing uncertainty cues are ineffective in social media context because of its specific characteristics. In this paper, we propose a variant of annotation scheme for uncertainty identification and construct the first uncertainty corpus based on tweets. We then conduct experiments on the generated tweets corpus to study the effectiveness of different types of features for uncertainty text identification.

Original languageEnglish
Title of host publicationProceedings of the 51st annual meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
Pages58-62
Number of pages5
Volume2
ISBN (Print)978-1-937284-51-0
Publication statusPublished - 2013
Event51st annual meeting of the Association for Computational Linguistics - Sofia, Bulgaria
Duration: 4 Aug 20139 Aug 2013

Meeting

Meeting51st annual meeting of the Association for Computational Linguistics
Abbreviated titleACL 2013
CountryBulgaria
CitySofia
Period4/08/139/08/13

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