在摩洛哥方言中使用自然语言处理与人工智能的实体识别
Wala Bin Subait1, Nazir Ahmad2, Muhammad Swaileh A Alzaidi3
1Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|August 22, 2025
概括
这项研究引入了阿拉伯语命名实体识别 (NER) 的新技术, 显著提高了准确性. 通过使用人工智能优化 (NGOAI-ANER) 方法提升了从阿拉伯文中提取实体的精度.
科学领域:
- 自然语言处理 (NLP)
- 人工智能 (AI)
- 深度学习 (DL)
背景情况:
- 命名实体识别 (NER) 对于NLP应用,如数据检索至关重要.
- 由于语言的复杂性,阿拉伯语NER面临着独特的挑战,特别是在方言中.
- 现有的深度学习模型通常集中在现代标准阿拉伯语 (MSA),忽视方言.
研究的目的:
- 为摩洛哥方言推出一种新的技术,即用人工智能优化阿拉伯名字实体识别 (NGOAI-ANER).
- 提高阿拉伯语NER系统的精度和效率.
- 通过使用先进的人工智能来解决方言阿拉伯语NER的复杂性.
主要方法:
- 词嵌入技术以语义表示文本.
- 堆叠的注意力长短内存 (SALSTM) 提供准确的实体识别.
- 北方Goshawk优化 (NGO) 模型用于DL模型的超参数调整.
主要成果:
- 在DarNERcorp数据集上,NGOAI-ANER技术表现出卓越的性能.
- 实现了97.86%的准确性,超过了现有的阿拉伯NER方法.
- 验证了拟议的人工智能驱动的NER模型的有效性和可扩展性.
结论:
- NGOAI-ANER技术在阿拉伯方言中提供了显著的进步.
- 优化算法与深度学习的整合有效地解决了NER的挑战.
- 这种方法为阿拉伯名字实体识别提供了可扩展和准确的解决方案.
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