MUD allows to detect uncertainty in natural language. It relies on a new supervised and generic approach based on the statistical analysis of multiple lexical and syntactic features used to characterize sentences through vector-based representations that can be analyzed by proven classification methods (like SVM).
You may found additional content in following publications:

• “Uncertainty detection in natural language: a probabilistic model”. Pierre-Antoine Jean, Sébastien Harispe, Sylvie Ranwez, Patrice Bellot, Jacky Montmain. In Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics (WIMS’16), Rajendra Akerkar, Michel Plantié, Sylvie Ranwez, Sébastien Harispe, Anne Laurent, Patrice Bellot, Jacky Montmain, and François Trousset (Eds.). ACM International Conference Proceeding Series, New York, NY, USA, Article 10, 10 pages. DOI: http://dx.doi.org/10.1145/2912845.2912873, ISBN: 978-1-4503-4056-4, Nîmes, France, June 13-15 2016.
• (in French only) “Un modèle probabiliste pour la détection de l’incertitude dans le langage naturel”. Pierre-Antoine Jean, Sébastien Harispe, Sylvie Ranwez, Patrice Bellot, Jacky Montmain. Actes de CORIA 2016

Source code available on GitHub.