一个全面的数据集和神经网络方法用于乌兹别克语语言的命名实体识别

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Davlatyor Mengliev^1,2, Vladimir Barakhnin^2,3, Mukhriddin Eshkulov⁴

¹Urgench branch of Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, 110, al-Khwarizmi str., 220100 Urgench city, Uzbekistan.

Data in brief

|January 15, 2025

概括

研究人员开发了一个新的乌兹别克斯坦命名实体识别数据集和CNN+LSTM模型. 这有助于乌兹别克自然语言处理,达到90.8%的F1得分.

科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
计算语言学计算语言学
机器学习机器学习

背景情况:

命名实体识别 (NER) 对于信息提取至关重要.
对于像乌兹别克语这样的低资源语言,资源有限.
现有的乌兹别克斯坦NER数据集和模型很少.

研究的目的:

在乌兹别克语语言中创建一个用于命名实体识别 (NER) 的新数据集.
训练和评估乌兹别克斯坦NER的机器学习模型.
推进乌兹别克语语言的NLP能力.

主要方法:

从法律文件和手工制作的句子中整理了2000个句子 (25,865个单词) 的数据集.
该数据集使用BIOES (开始,内部,外部,结束,单一) 标记方案进行了注释.
结合长期短期记忆 (LSTM) 架构的卷积神经网络 (CNN) 用于NER模型训练.

主要成果:

在测试组中,CNN+LSTM模型取得了高性能.
实现了F1得分90.8%,精度93.9%,以及Uzbek NER.的召回率为88.0%.
开发的数据集和模型显示了乌兹别克斯坦NLP任务的巨大潜力.

结论:

创建的乌兹别克斯坦NER数据集和训练的CNN+LSTM模型代表了对乌兹别克斯坦NLP的宝贵贡献.
该研究为乌兹别克斯坦NER的未来研究提供了一个基准.
未来的工作可能涉及数据集的扩展和探索替代的神经网络架构.

关键词:

语言库语言库.语言学研究的研究.资源较少的语言资源较少的语言命名实体是指有名称的实体.乌兹别克语语言乌兹别克语语言

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