FAR-AM:用于火灾原因分类的混合关注框架
1School of Management, China University of Mining and Technology (Beijing), Beijing, China.
PloS one
|October 9, 2025
概括
一个新的混合深度学习模型,火灾事故报告注意力机制 (FAR-AM),有效地分类火灾事故报告. 这种方法提高了复杂的,特定领域的文本分析的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 消防事故报告的自动分类 (FIREAR) 对公共安全和预防至关重要.
- 现有的深度学习模型面临着长期,杂和碎片化的火灾报告的挑战.
研究的目的:
- 开发一种新的混合深度学习框架,FAR-AM,以解决分类火灾事故报告的局限性.
- 提高自动化对特定领域文档的原因分类的准确性和稳定性.
主要方法:
- 使用大型语言模型 (LLM) 来预处理冗长的报告,将其转化为简短的摘要.
- 开发了一种层间自我注意机制,以融合层次的BERT功能.
- 使用TextCNN用于最终分类化特征.
主要成果:
- 在FIREAR数据集上,FAR-AM实现了73.58%的准确性和70.65%的F1得分.
- 在各种基准上表现优于强大的变压器基线,如RoBERTa.
- 废弃性研究证实了FAR-AM框架中的每个成分的有效性.
结论:
- 像FAR-AM这样的专用混合架构对于复杂的,特定领域的NLP任务比通用模型更有效.
- 对于分析具有挑战性的火灾事故报告数据,FAR-AM提供了一个强大的解决方案.
- 拟议的方法通过改进数据驱动的预防策略来提高公共安全.
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