基于自主关注BiLSTM-CRF的上下文嵌入的准确灾难实体识别
Noor E Hafsa1, Hadeel Mohammed Alzoubi1, Atikah Saeed Almutlq1
1Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudi Arabia.
PloS one
|March 26, 2025
概括
本研究引入了一种先进的命名实体识别 (NER) 模型,用于从新闻中提取关键的灾害信息. 该模型显著改善了用于应急响应的灾难数据收集.
科学领域:
- 自然语言处理自然语言处理.
- 提取信息 提取信息
- 灾害管理 灾害管理
背景情况:
- 自动提取与灾难相关的命名实体对于及时收集危机信息至关重要.
- 在线新闻是紧急情况和应对期间灾难信息的关键来源.
- 有效的灾害管理依赖于准确和可访问的危机数据.
研究的目的:
- 调查在线新闻文章中自动提取与灾难相关的命名实体.
- 为 NER 开发和评估一种新的,背景化的深度双向 LSTM 网络.
- 将拟议模型的性能与现有的词嵌入方法进行比较.
主要方法:
- 构建了一个新的词嵌入模型,灵感来自Word2vec,用于上下文向量表示.
- 使用深度双向LSTM网络,具有自我注意和CRF层来编码功能.
- 标注了1000篇在线新闻文章的数据集,其中有14个危机特定实体.
主要成果:
- 拟议的NER模型在独立测试组中实现了句子级精度92%,回忆率91%,准确率87%,F1得分92%的结果.
- 情境敏感的优化模型表现优于一般的,非情境词嵌入和BERT模型.
- 在提取与灾难相关的命名实体方面表现出卓越的性能.
结论:
- 开发的情境敏感的NER模型在自动化灾害信息提取方面取得了重大进展.
- 这种方法提高了对灾害管理至关重要的数据的可靠性和及时性.
- 该模型的有效性凸显了在危机信息学中语境词嵌入的重要性.
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