Industry-wide nuclear power plant operating experience is a critical source of raw data for performing parameter estimations in reliability and risk models. Much operating experience information pertains to failure events and is stored as reports containing unstructured data, such as narratives. Event reports are essential for understanding how failures are initiated and propagated, including the numerous causal relations involved. Causal relation extraction using deep learning represents a significant frontier in the field of natural language processing (NLP), and is crucial since it enables the interpretation of intricate narratives and connections contained within vast amounts of written information. This paper proposed a hybrid framework for causality detection and extraction from nuclear licensee event reports. The main contributions include: (1) we compiled an LER corpus with 20,129 text samples for causality analysis, (2) developed an interactive tool for labeling cause effect pairs, (3) built a deep-learning-based approach for causal relation detection, and (4) developed a knowledge based cause-effect extraction approach.
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；University Of Electronic Science And Technology Of China（电子科技大学）
Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings
Sun Yat-Sen University（中山大学）
从自然语言文本中提取因果关系是人工智能中一个具有挑战性的开放性问题。现有的方法利用模式、约束和机器学习技术来提取因果关系，这在很大程度上依赖于领域知识，并且需要相当多的人力和时间来进行特征工程。在本文中，我们制定了一个新的因果关系标记方案的基础上，因果关系提取序列标记问题。在此基础上，我们提出了一种以BiLSTM-CRF模型为主干的神经因果关系提取器SCITE（Self-attentive BiLSTM-CRF wIth Transferred Embeddings），它可以直接提取因果关系，而不需要单独提取候选因果对并识别它们之间的关系。为了解决数据不足的问题，我们转移了上下文字符串嵌入，也称为Flair嵌入，在我们的任务中，它是在大型语料库上训练的。此外，为了提高因果关系抽取的性能，我们在SCITE中引入了多头自注意力机制来学习因果词之间的依赖关系。我们在一个公共数据集上对我们的方法进行了评估，实验结果表明，与基线相比，我们的方法实现了显著且一致的改进。
Causal Knowledge Extraction from Scholarly Papers in Social Sciences
Belk College of Business, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校贝尔克商学院）；Duke University（杜克大学）；College of Computing and Informatics, University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校计算与信息学院）
Event Causality Extraction with Event Argument Correlations
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络空间安全学院）；Beijing University of Posts and Telecommunications（北京邮电大学）
Hallucination-Resistant Relation Extraction via Dependency-Aware Sentence Simplification and Two-tiered Hierarchical Refinement
Shanghai Jiao Tong University（上海交通大学）；University of California San Diego（加州大学圣地亚哥分校）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Alibaba Group（阿里巴巴集团）
Fine-Grained Causality Extraction From Natural Language Requirements Using Recursive Neural Tensor Networks
Qualicen GmbH（Qualicen有限公司）；Technical University of Munich（慕尼黑工业大学）；Blekinge Institute of Technology（布莱金厄理工学院）；University of Cologne（科隆大学）；fortiss GmbH（福蒂斯有限公司）
