基于外部上下文检索和对抗性培训的汽车故障文本的中国命名实体识别
Shuhai Wang1,2, Linfu Sun1,2
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种新的中国命名实体识别 (NER) 方法,用于汽车故障文本. 该方法通过利用外部上下文检索和对抗性培训来加强故障诊断,以改进关键概念的识别.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 汽车工程 汽车工程
背景情况:
- 在汽车故障文本中有效挖掘关键概念对于准确的诊断至关重要.
- 当前的工具缺乏探索这些文本中隐藏的信息的能力,阻碍了全面的理解.
研究的目的:
- 开发一个针对汽车故障文本的中国命名实体识别 (NER) 模型.
- 通过先进的文本挖掘,提高对故障原因的理解,提高诊断能力.
主要方法:
- 通过搜索引擎利用外部上下文检索.
- 员工词典 增强的BERT用于改进文本嵌入式表示输入句子和外部上下文.
- 集成了一个注意力机制,用于融合文本和上下文嵌入.
- 通过扰乱融合矢量表示生成对抗样本.
- 应用BiLSTM-CRF层用于使用融合表示和对抗样本进行实体标签.
主要成果:
- 在汽车故障数据集上取得了最先进的结果.
- 在识别汽车故障文本中的关键概念方面表现卓越.
- 通过外部上下文集成成功改进了文本嵌入式表示.
结论:
- 拟议的中国NER模型有效地识别了汽车故障文本中的关键概念.
- 外部上下文检索和对抗性培训显著提高了错误文本分析的性能.
- 该模型为探索隐藏信息和改进汽车故障诊断提供了一个有前途的解决方案.
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