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使用微调BiLSTM框架检测乌尔都语文的副词检测

Muhammad Ali Aslam¹, Khairullah Khan¹, Wahab Khan¹

¹Department of Computer Science, University of Science and Technology, Bannu, 28100, Pakistan.

Scientific reports

|May 2, 2025

概括

这项研究引入了一种新的双向长期短期记忆 (BiLSTM) 框架,用于乌尔都语的自动化转句检测,在定制的语料库上达到94.14%的准确性. 这项研究还提出了一个大规模的乌尔都语拼写体 (UPC),以推进NLP研究.

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
人工智能 (AI) 是一种人工智能.

背景情况:

自动转述检测对于NLP任务,如总结和抄袭检测至关重要.
由于复杂的形态学,脚本和有限的资源,乌尔都语释检测面临着挑战.
现有的方法与乌尔都语的语言细微差别作斗争.

研究的目的:

开发一个强大的框架来检测乌尔都语的重复表达.
为了解决乌尔都语语言的复杂性,在表述识别中.
为乌尔都语NLP研究创造一个有价值的资源.

主要方法:

提出了一个新的双向长期短期记忆 (BiLSTM) 框架.
使用了词嵌入和文本预处理 (标记化,停止词删除,标签编码).
开发了一个大规模的乌尔都语抄本库 (UPC),包含15万个手动验证的抄本对.

主要成果:

在定制的UPC数据集上,BiLSTM模型实现了94.14%的准确性.
超越了卷积神经网络 (CNN) 的83.43%和长期短期记忆 (LSTM) 的88.09%.
在基准Quora数据集上达到95.34%的准确性,证明了广泛的适用性.

结论:

拟议的BiLSTM框架显著提高了乌尔都语转述检测性能.
创建的乌尔都语抄本库 (UPC) 作为未来研究的关键资源.
对于特殊情况下,语言规则引擎增强了模型的稳定性.

关键词:

这就是BiLSTM.在美国,CNN是CNN.这是LSTM的LSTM.在NLP中,我们使用了NLP.释检测检测释检测检测乌尔都语文的文字是乌尔都语.

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