Paper: Uncertainty Detection in Hungarian Texts

ACL ID C14-1174
Title Uncertainty Detection in Hungarian Texts
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014

Uncertainty detection is essential for many NLP applications. For instance, in information re- trieval, it is of primary importance to distinguish among factual, negated and uncertain informa- tion. Current research on uncertainty detection has mostly focused on the English language, in contrast, here we present the first machine learning algorithm that aims at identifying linguistic markers of uncertainty in Hungarian texts from two domains: Wikipedia and news media. The system is based on sequence labeling and makes use of a rich feature set including orthographic, lexical, morphological, syntactic and semantic features as well. Having access to annotated data from two domains, we also focus on the domain specificities of uncertainty detection by compar- ing results obtained in indomain ...