Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2013
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.
Keywords
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Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 51th Annual Meeting of the Association for Computational Linguistics (ACL 2013)
First Page
58
Last Page
62
Publisher
Association for Computational Linguistics
City or Country
Sofia, Bulgaria
Citation
WEI, Zhongyu; CHEN, Junwen; GAO, Wei; LI, Binyang; ZHOU, Lanjun; HE, Yulan; and WONG, Kam-Fai.
An empirical study on uncertainty identification in social media context. (2013). Proceedings of the 51th Annual Meeting of the Association for Computational Linguistics (ACL 2013). 58-62.
Available at: https://ink.library.smu.edu.sg/sis_research/4584
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://aclweb.org/anthology/P13-2011