Paper: An Empirical Study on Uncertainty Identification in Social Media Context

ACL ID P13-2011
Title An Empirical Study on Uncertainty Identification in Social Media Context
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2013
Authors

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 in- effective in social media context because of its specific characteristics. In this pa- per, 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.