research-article

Uncertainty detection in natural language: a probabilistic model

Authors:

Pierre-Antoine Jean,

Sébastien Harispe,

Patrice Bellot,

Jacky MontmainAuthors Info & Claims

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

Article No.: 10, Pages 1 - 10

https://doi.org/10.1145/2912845.2912873

Published: 13 June 2016 Publication History

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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Lu JZhang HXiao YWang Y(2024)An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence ApproachJMIR AI10.2196/472403(e47240)Online publication date: 29-Jan-2024
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Uncertainty detection in natural language: a probabilistic model
1. Computing methodologies
  1. Artificial intelligence

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cover image ACM Other conferences

WIMS '16: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

June 2016

309 pages

ISBN:9781450340564

DOI:10.1145/2912845

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 June 2016

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WIMS '16: International Conference on Web Intelligence, Mining and Semantics

June 13 - 15, 2016

Nîmes, France

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WIMS '16 Paper Acceptance Rate 36 of 53 submissions, 68%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Lu JZhang HXiao YWang Y(2024)An Environmental Uncertainty Perception Framework for Misinformation Detection and Spread Prediction in the COVID-19 Pandemic: Artificial Intelligence ApproachJMIR AI10.2196/472403(e47240)Online publication date: 29-Jan-2024
https://doi.org/10.2196/47240
Pan QYang PZhang J(2024)BayesTSF: Measuring Uncertainty Estimation in Industrial Time Series Forecasting from a Bayesian PerspectiveAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5581-3_7(81-93)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-981-97-5581-3_7
Diaf SSchütze F(2024)Uncovering Uncertainty in Narrative Economics: A Semantic Search ApproachNew Frontiers in Textual Data Analysis10.1007/978-3-031-55917-4_26(323-335)Online publication date: 24-Sep-2024
https://doi.org/10.1007/978-3-031-55917-4_26
Boguslav MSalem NWhite ESullivan KBada MHernandez TLeach SHunter L(2023)Creating an ignorance-base: Exploring known unknowns in the scientific literatureJournal of Biomedical Informatics10.1016/j.jbi.2023.104405143(104405)Online publication date: Jul-2023
https://doi.org/10.1016/j.jbi.2023.104405
Solarte Pabón OMontenegro OTorrente MRodríguez González AProvencio MMenasalvas E(2022)Negation and uncertainty detection in clinical texts written in Spanish: a deep learning-based approachPeerJ Computer Science10.7717/peerj-cs.9138(e913)Online publication date: 7-Mar-2022
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Ebert FCastor FNovielli NSerebrenik A(2021)An exploratory study on confusion in code reviewsEmpirical Software Engineering10.1007/s10664-020-09909-526:1Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s10664-020-09909-5
Sinha MAgarwal NDasgupta THuang RWu DMarchionini GHe DCunningham SHansen P(2020)Relation Aware Attention Model for Uncertainty Detection in TextProceedings of the ACM/IEEE Joint Conference on Digital Libraries in 202010.1145/3383583.3398613(437-440)Online publication date: 1-Aug-2020
https://dl.acm.org/doi/10.1145/3383583.3398613
Sinha MDasgupta T(2020)Detecting Uncertainty in Text using Multi-Channel CNN-TreeBiLSTM NetworkCompanion Proceedings of the Web Conference 202010.1145/3366424.3382713(92-93)Online publication date: 20-Apr-2020
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Omero PValotto MBellana RBongelli RRiccioni IZuczkowski ATasso C(2020)Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause taggingLanguage Resources and Evaluation10.1007/s10579-020-09491-854:4(1161-1181)Online publication date: 11-Jun-2020
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Isenegger KDong YShang MFurst JStan-Raicu D(2019)Characterizing and Quantifying Diagnostic (Un)Certainty in Medical Reports through Natural Language Processing2019 International Conference on Computational Science and Computational Intelligence (CSCI)10.1109/CSCI49370.2019.00174(914-919)Online publication date: Dec-2019
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