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相关概念视频

Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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.
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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使用机器学习算法检测资源较差的语言中的冒犯性术语.

Muhammad Owais Raza1, Naeem Ahmed Mahoto1, Mohammed Hamdi2

  • 1Department of Software Engineering, Mehran University of Engineering and Technology Jamshoro, Jamshoro, Pakistan.

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概括

这项研究开发了一种新方法,可以自动检测乌尔都语社交媒体内容中的冒犯性术语. 这种方法可以在Twitter和YouTube等平台上提高低资源语言的准确性.

关键词:
分类模型的分类模型.机器学习是机器学习.冒犯性术语是一种冒犯性术语.资源较差的语言

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科学领域:

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

背景情况:

  • 用户生成的冒犯性内容给社交媒体平台带来了挑战.
  • 自动检测冒犯性术语是很困难的,特别是对于乌尔都语等资源较低的语言.
  • 现有的NLP努力主要集中在高资源语言上,为乌尔都语留下了一个空白.

研究的目的:

  • 引入一种组合式预处理方法来检测乌尔都语的冒犯性术语.
  • 开发和评估乌尔都语攻击性内容的跨平台分类模型.
  • 用乌尔都语的各种预处理技术来评估机器学习模型的性能.

主要方法:

  • 利用来自Twitter和YouTube的数据集进行培训和测试.
  • 实现了决策树,随机森林和天真的贝叶斯算法.
  • 应用了结合式预处理方法,结合了诸如停词和标点删除等技术.

主要成果:

  • 结合式预处理方法在乌尔都语攻击性术语检测方面表现出有效性.
  • 在D1的训练和D2.2的测试中,删除止词的准确率达到了83.27%.
  • 在D2的训练和D1.1的测试中,停止词和标点删除在D2的训练和测试中达到74.54%的准确性.
  • 拟议的方法超过了基准标准,在D1和D2数据集上分别达到82.9%和97.2%的准确性.

结论:

  • 组合式预处理方法对于构建乌尔都语攻击术语检测模型是有效的.
  • 机器学习模型显示基于跨平台设置中的预处理组合的不同性能.
  • 这项研究有助于应对低资源语言中攻击性内容检测的挑战.