一种基于数据和模型的基于人工智能的新方法,用于开发高性能Bengali语言命名实体识别.
Khadija Akter Lima1, Khan Md Hasib2, Sami Azam3
1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
PloS one
|September 22, 2023
概括
本研究引入了孟加拉语命名实体识别 (NER) 的新方法,通过整合数据和以模型为中心的AI来实现最先进的结果. 开发的混合模型显著提高了孟加拉语NLP任务的准确性.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 命名实体识别 (NER) 对特定领域的NLP应用至关重要,但由于语言复杂性和资源稀缺性,现有的孟加拉语NER模型缺乏准确性.
- 像英语和中文这样的高资源语言具有表现良好的NER架构,突出了像孟加拉语这样的形态丰富语言的差距.
研究的目的:
- 通过结合以数据为中心和以模型为中心的AI,实现孟加拉语命名实体识别 (NER) 的最先进性能.
- 提出一种开发高质量的NER数据集的方法,适用于任何语言.
- 评估高质量的数据集对NER模型准确性的影响.
主要方法:
- 专门为孟加拉语NER开发了一个独特的,高质量的数据集.
- 集成的以数据为中心的AI (数据集质量) 和以模型为中心的AI (混合深度学习模型) 方法.
- 使用创建的数据集评估了各种深度学习模型.
主要成果:
- 一个混合型号实现了精确匹配F1得分87.50%,部分匹配F1得分92.31%,微型F1得分98.32%.
- 该研究表明,高质量的数据集对NER模型性能产生了重大影响.
- 拟议的模型最大限度地减少了手动功能工程的需要,并且需要更少的计算资源.
结论:
- 综合人工智能方法和高质量的数据集显著提升了孟加拉语NER的能力.
- 开发的数据集创建方法可以将其推广到其他语言.
- 这项工作为形态丰富的语言中NER提供了资源高效和准确的解决方案.
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