Uncertainty detection in natural language: a probabilistic model

Abstract : Designing approaches able to automatically detect uncertain expressions within natural language is central to design efficient models based on text analysis, in particular in domains such as question-answering, approximate reasoning, knowledge-based population. This article proposes an overview of several contributions and classifications defining the concept of uncertainty expressions in natural language, and the related detection methods that have been proposed so far. A new supervised and generic approach is next introduced for this specific task; it is 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. The global performance of our approach is demonstrated and discussed with regard to various dimensions of uncertainty and text specificities. This method is available for download at https://github.com/pajean/uncertaintyDetection.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01484994
Contributor : Sébastien Harispe <>
Submitted on : Wednesday, March 8, 2017 - 9:49:35 AM
Last modification on : Monday, February 11, 2019 - 6:22:02 PM

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Pierre-Antoine Jean, Sébastien Harispe, Sylvie Ranwez, Patrice Bellot, Jacky Montmain. Uncertainty detection in natural language: a probabilistic model. WIMS '16 Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, Jun 2016, Nîmes, France. ⟨10.1145/2912845.2912873⟩. ⟨hal-01484994⟩

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