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Detecting Uncertainty in Text using Multi-Channel CNN-TreeBiLSTM Network

Authors:

Manjira Sinha,

Tirthankar DasguptaAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 92 - 93

https://doi.org/10.1145/3366424.3382713

Published: 20 April 2020 Publication History

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Abstract

Uncertainty in a text refers to the propositions that exhibit fuzziness in meaning. In this paper, we present a novel Multi-Channel Tree-LSTM model that integrates a relation aware self-attention along with multiple embeddings to automatically detect uncertainty cues in texts. We have evaluated the models with data sources across multiple domains that include bio-medical texts, privacy policies, and consumer reviews. Our preliminary analysis showed that the proposed model outperforms the existing baseline systems across all the domains.

References

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Cited By

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Kouadri WAkoka JComyn-Wattiau Idu Mouza C(2022)Uncertainty Detection in Historical DatabasesNatural Language Processing and Information Systems10.1007/978-3-031-08473-7_7(73-85)Online publication date: 13-Jun-2022
https://doi.org/10.1007/978-3-031-08473-7_7
Marapelli BCarie AIslam S(2020)RNN-CNN MODEL:A Bi-directional Long Short-Term Memory Deep Learning Network For Story Point Estimation2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)10.1109/CITISIA50690.2020.9371770(1-7)Online publication date: 25-Nov-2020
https://doi.org/10.1109/CITISIA50690.2020.9371770

Index Terms

Detecting Uncertainty in Text using Multi-Channel CNN-TreeBiLSTM Network
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems

Index terms have been assigned to the content through auto-classification.

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Published In

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

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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Association for Computing Machinery

New York, NY, United States

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Published: 20 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Kouadri WAkoka JComyn-Wattiau Idu Mouza C(2022)Uncertainty Detection in Historical DatabasesNatural Language Processing and Information Systems10.1007/978-3-031-08473-7_7(73-85)Online publication date: 13-Jun-2022
https://doi.org/10.1007/978-3-031-08473-7_7
Marapelli BCarie AIslam S(2020)RNN-CNN MODEL:A Bi-directional Long Short-Term Memory Deep Learning Network For Story Point Estimation2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA)10.1109/CITISIA50690.2020.9371770(1-7)Online publication date: 25-Nov-2020
https://doi.org/10.1109/CITISIA50690.2020.9371770

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Relation Aware Attention Model for Uncertainty Detection in Text

Detecting hedge cues and their scope in biomedical text with conditional random fields

Uncertainty Quantification for Text Classification

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