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相关概念视频

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

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The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Aggregates Classification01:29

Aggregates Classification

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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
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How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Relative Frequency Histogram01:14

Relative Frequency Histogram

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The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...
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相关实验视频

Updated: May 14, 2025

Examining Online Syntactic Processing of Spoken Complex Sentences in Chinese Using Dual-Modal Interference Tasks
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一个数据集用于文档级别的中国金融事件提取.

Yubo Chen1,2, Tong Zhou3, Sirui Li4

  • 1The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China. yubo.chen@nlpr.ia.ac.cn.

Scientific data
|May 10, 2025
PubMed
概括

本研究介绍了DocFEE,这是一个大型数据集,用于从长长的中国文件中提取金融事件. 它解决了现有数据集对现实世界金融事件建模的局限性.

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科学领域:

  • 自然语言处理自然语言处理.
  • 金融技术 金融技术
  • 数据科学数据科学数据科学

背景情况:

  • 金融事件建模对于投资决策和风险管理至关重要.
  • 传统方法依赖于昂贵的专家分析.
  • 现有的数据集不适合用于金融领域常见的长文件.

研究的目的:

  • 解决当前数据集在金融事件提取中的局限性.
  • 提出一个大规模的数据集,用于文档级别的中国金融事件提取.
  • 为了促进更准确,更有效的金融事件建模.

主要方法:

  • 开发DocFEE,这是一个用于文档级金融事件提取的新型数据集.
  • 专注于中国的财务公告文件.
  • 捕捉事件参数之间的长距离依赖关系.

主要成果:

  • DocFEE是一个大规模的数据集,反映了现实世界的文档长度.
  • 数据集捕获了长距离参数依赖关系.
  • 能够从长长的文本中提取更好的自动化金融事件.

结论:

  • 在金融事件提取中,DocFEE增强了自然语言处理的能力.
  • 该数据集支持更强大的金融事件建模用于投资和风险管理.
  • 解决了中国金融文本分析现有资源的严重缺口.