Uncertainty detection is essential for many NLP applications. For instance, in information retrieval, it is of primary importance to distinguish among factual, negated and uncertain information. 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 comparing results obtained in indomain and cross-domain settings. Our results show that the domain of the text has significant influence on uncertainty detection.
Most of these systems focus on the English language, however, we are aware of a study aiming at detecting uncertainty in Hungarian texts REF .
Uncertainty Detection in Hungarian Texts
COLING
Computer Science