基于双流功能补充的嵌套命名实体识别
Tao Liao1, Rongmei Huang1, Shunxiang Zhang1
1College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Entropy (Basel, Switzerland)
|July 8, 2023
概括
本研究引入了一种新的双流特征补充模型,用于自然语言处理中的嵌套命名实体识别 (NER). 该模型显著改善了特征提取,以增强对复杂文本数据的理解.
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
- 计算语言学 计算语言学
背景情况:
- 嵌套的命名实体很普遍,对于各种自然语言处理 (NLP) 任务至关重要.
- 现有的模型往往难以有效地从复杂的嵌套实体结构中提取特征.
研究的目的:
- 提出一个高效的嵌套命名实体识别模型,使用双流特征互补性.
- 增强从文本中提取低层次和深层次的语义信息,以提高NER性能.
主要方法:
- 句子在单词和字符层面都嵌入.
- 双LSTM网络捕获句子上下文,然后是低级特征互补性.
- 多头注意力和高级功能补充模块提取深层次的语义信息.
主要成果:
- 拟议的模型显示了特征提取能力的显著改进.
- 实验结果显示,与经典模型相比,在识别嵌套的命名实体方面,性能提高了.
结论:
- 双流特征互补性有效地捕获嵌套NER的丰富语义信息.
- 该模型为推进命名实体识别系统的准确性和效率提供了一个有希望的方法.
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