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相关概念视频

Language Development01:22

Language Development

317
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
317
Language and Cognition01:27

Language and Cognition

323
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
323

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相关实验视频

Updated: Jun 5, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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基于机器学习的框架,用于细粒度的单词细分和增强的文本规范化,用于低资源语言.

Shahzad Nazir1, Muhammad Asif1, Mariam Rehman2

  • 1Department of Computer Science, National Textile University, Faisalabad, Pakistan.

PeerJ. Computer science
|December 13, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了乌尔都语的高级文本规范化和令牌化方法,增强自然语言处理 (NLP) 的结果. 这些技术显著改善了乌尔都文本预处理,解决了对这种广泛使用的语言的研究缺口.

关键词:
资源较少的语言 资源较少的语言机器学习是机器学习.文本规范化 文本规范化单词细分 词汇的细分

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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.
  • 乌尔都语语言技术

背景情况:

  • 文本预处理,包括规范化和令牌化,对于有效的自然语言处理 (NLP) 是至关重要的.
  • 现有的NLP工具经常忽视第十大最常说的语言,乌尔都语,尽管它具有全球意义.
  • 对于乌尔都语,需要专门和改进的预处理技术.

研究的目的:

  • 为乌尔都语开发和介绍增强的文本规范化技术.
  • 引入专门为乌尔都文本设计的改进的词代币化方法.
  • 解决NLP社区内乌尔都语预处理方面的研究缺口.

主要方法:

  • 乌尔都文本规范化使用正则表达式和基于规则的系统的组合,包括字符规范化和数字分离.
  • 乌尔都语单词代币化采用机器学习模型与手工制作的功能来预测单词边界.
  • 创建了最大的人类注释的乌尔都语数据集,跨越五个不同的领域,用于模型培训和评估.

主要成果:

  • 拟议的规范化方法在乌尔都文本预处理中实现了20%的改进.
  • 开发的代币化方法导致乌尔都语单词细分率提高6%.
  • 包括精度,回忆,F测量和准确性在内的评估指标证明了与最先进的方法相比,拟议的技术的有效性.

结论:

  • 实施的文本规范化和令牌化技术为乌尔都语语言处理提供了显著的进步.
  • 这些方法提高了涉及乌尔都文本的自然语言处理任务的准确性和效率.
  • 该研究为乌尔都语NLP提供了宝贵的资源和方法,为未来的研究和应用铺平了道路.