基于深度学习的阿姆哈拉语语言的成语表达式识别
Demeke Endalie1, Getamesay Haile1, Wondmagegn Taye2
1Faculty of Computing and Informatics, Jimma Institute of Technology, Jimma, Ethiopia.
PloS one
|December 14, 2023
概括
这项研究介绍了一个卷积神经网络 (CNN) 与快速文本用于阿姆哈拉语方言检测. 该模型实现了80%的准确性,改善了自然语言处理任务.
科学领域:
- 计算语言学 计算语言学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 语言表达式对自然语言处理 (NLP) 具有挑战性,因为它们的非字面意义.
- 现有的阿姆哈拉语NLP模型经常忽略成语,影响机器翻译和情感分析等任务的性能.
- 语在阿姆哈拉语对话和文学中很普遍.
研究的目的:
- 提出和评估一种用于检测阿姆哈拉文本中方言表达式的新型模型.
- 解决当前NLP模型在处理阿姆哈拉语成语方面的局限性.
- 通过结合方言检测来提高各种阿姆哈拉语NLP应用程序的准确性.
主要方法:
- 开发了一个卷积神经网络 (CNN) 模型,与FastText字嵌入集成.
- 从书籍中收集了1700个成语和1600个非成语的阿姆哈拉语表达式的数据集.
- 训练并测试模型,使用80/10/10数据分割进行训练,验证和测试.
主要成果:
- 拟议的CNN-FastText模型在培训数据集上实现了98%的学习准确性.
- 该模型在未见测试数据集上显示了80%的准确性.
- 性能与传统的机器学习分类器 (如KNN,SVM和Random Forest) 相比较有利.
结论:
- 美国有线电视新闻网 (CNN) -FastText模型显示,在阿姆哈拉语中准确的成语表达式检测方面,有显著的前景.
- 这种方法可以提高阿姆哈拉语下游NLP任务的性能.
- 进一步的研究可以建立在这个模型上,以增强人工智能系统中的阿姆哈拉语语言理解.
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