Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework

Shahidur  Rahoman Sohag; Sai Zhang; Min Xian; Shoukun Sun; Fei Xu; Zhegang Ma

doi:10.13182/T130-44918

Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework

Shahidur Rahoman Sohag, Sai Zhang, Min Xian, Shoukun Sun, Fei Xu, Zhegang Ma

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this paper, we proposed a hybrid framework for causality detection and extraction in LER documents. The main contributions are summarized below. We 1) built an LER corpus with 20,129 text samples for causality analysis, 2) developed an interactive tool was developed for labeling cause-effect pairs, 3) built a deep learning-based approach for causal relation detection, and 4) built a knowledge-based cause-effect extraction approach.

Original language	American English
Pages (from-to)	840-843
Journal	Transactions of the American Nuclear Society
Volume	130
Issue number	1
Early online date	Jun 2024
DOIs	https://doi.org/10.13182/T130-44918
State	Published - Jun 2024

INL Publication Number

INL/CON-24-76755
169909

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10.13182/T130-44918

https://www.ans.org/pubs/transactions/article-56174/

Cite this

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title = "Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework",

abstract = "In this paper, we proposed a hybrid framework for causality detection and extraction in LER documents. The main contributions are summarized below. We 1) built an LER corpus with 20,129 text samples for causality analysis, 2) developed an interactive tool was developed for labeling cause-effect pairs, 3) built a deep learning-based approach for causal relation detection, and 4) built a knowledge-based cause-effect extraction approach.",

author = "{Rahoman Sohag}, Shahidur and Sai Zhang and Min Xian and Shoukun Sun and Fei Xu and Zhegang Ma",

year = "2024",

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AU - Rahoman Sohag, Shahidur

AU - Zhang, Sai

AU - Xian, Min

AU - Sun, Shoukun

AU - Xu, Fei

AU - Ma, Zhegang

PY - 2024/6

Y1 - 2024/6

N2 - In this paper, we proposed a hybrid framework for causality detection and extraction in LER documents. The main contributions are summarized below. We 1) built an LER corpus with 20,129 text samples for causality analysis, 2) developed an interactive tool was developed for labeling cause-effect pairs, 3) built a deep learning-based approach for causal relation detection, and 4) built a knowledge-based cause-effect extraction approach.

AB - In this paper, we proposed a hybrid framework for causality detection and extraction in LER documents. The main contributions are summarized below. We 1) built an LER corpus with 20,129 text samples for causality analysis, 2) developed an interactive tool was developed for labeling cause-effect pairs, 3) built a deep learning-based approach for causal relation detection, and 4) built a knowledge-based cause-effect extraction approach.

UR - https://www.osti.gov/biblio/2356773

U2 - 10.13182/T130-44918

DO - 10.13182/T130-44918

M3 - Conference article

SN - 0003-018X

VL - 130

SP - 840

EP - 843

JO - Transactions of the American Nuclear Society

JF - Transactions of the American Nuclear Society

IS - 1

ER -

Causality Extraction from Nuclear Licensee Event Reports Using a Hybrid Framework

Abstract

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