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相关概念视频

Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Language Development01:22

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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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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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相关实验视频

Updated: May 9, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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一个时间知识图生成数据集,由大型语言模型远程监督.

Jun Zhu1, Yan Fu1,2, Junlin Zhou1,2

  • 1Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China.

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概括

本研究引入了一种新的方法,通过从文档中提取事实及其时间来构建时间知识图 (TKG). 这项研究提出了一个新的数据集和基于LLM的框架,以捕捉时间关系,增强信息检索.

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科学领域:

  • 自然语言处理自然语言处理.
  • 提取信息 提取信息
  • 知识表示 知识表示

背景情况:

  • 知识图表将事实表示为三重 (实体,关系,实体).
  • 现有的方法往往忽略了时间信息,限制了对动态关系的理解.
  • 文档级关系提取对于构建全面的知识图表至关重要.

研究的目的:

  • 解决从非结构化文本构建时间知识图 (TKG) 的差距.
  • 开发一种新的数据集和方法来从文档中提取时间信息.
  • 评估大型语言模型 (LLM) 在时间关系提取中的有效性.

主要方法:

  • 通过挖掘关系模式并将事实与时间结合起来,构建了一个新的数据集,以形成时间四倍.
  • 利用LLM生成时间四倍数,用于缺乏明确时间的三倍数.
  • 实现多个过器和手动注释,以确保数据质量.
  • 提出了一个基于LLM的框架,将关系提取转换为序列对序列任务,用于预测与时间的关系.

主要成果:

  • 证明了从文件中构建时间知识图的可行性.
  • 展示了LLMs在使用拟议的数据集和框架提取与时间信息的关系方面的表现.
  • 验证了新创建的时间数据集的质量和实用性.

结论:

  • 开发的数据集和基于LLM的框架显著推进了时间知识图表构建领域.
  • 整合时间信息可以更全面地理解文档中的事实联系.
  • 在LLM中,从文本中提取复杂的时间关系具有很强的潜力.