相关实验视频
基于文本描述的"集群然后估计"自然语言处理 (NLP) 方法来分类基于文本描述的海事事件严重性
Tianyi Chen1, Maohan Liang2, Wei Siong Lee1
1Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576.
Accident; analysis and prevention
|January 23, 2026
概括
这项研究引入了一种新的NLP方法,使用隐性迪里克莱特分配 (LDA) 和来自变压器的双向编码器表示 (BERT) 来自动估计文本的海上事件严重性. 该方法显著提高了评估船舶风险和管理事故数据的准确性.
科学领域:
- 海上安全的航行.
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 从文本描述中手动估计海事事件严重程度对于大型数据集来说是低效的.
- 目前的方法缺乏有效的风险评估和历史数据管理所需的速度和准确性.
研究的目的:
- 使用自然语言处理 (NLP) 开发和验证用于估计海上事故严重性的自动化方法.
- 提高海事行业事件严重性评估的效率和准确性.
主要方法:
- 采用"集群然后估计"策略,利用隐性迪里克莱特分配 (LDA) 进行文本预处理和集群.
- 从变压器 (BERT) 的双向编码器表示模型在每个集群中进行了微调以进行严重性估计.
- 根据严重程度分类的22,458起海事事故的数据集被用于培训和验证.
主要成果:
- 拟议的"集群然后估计"方法在与最先进的基线模型相比显示出更高的性能.
- 该方法准确地估计了事件严重程度,超过了现有的技术.
- 在LDA产生的集群中微调BERT显著提高了估计能力.
结论:
- 基于NLP的"集群然后估计"方法为自动化事件严重程度估计提供了实用和有价值的解决方案.
- 这种方法为改善海事事件评估和决策过程提供了显著的好处.
- 这项研究代表了NLP在基于文本数据的海上事件严重性分析中的开创性应用.
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