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相关概念视频

Aggregates Classification01:29

Aggregates Classification

313
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...
313
Classification of Systems-II01:31

Classification of Systems-II

138
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
138
Classification of Systems-I01:26

Classification of Systems-I

178
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
178
Classification of Signals01:30

Classification of Signals

430
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
430
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

32.2K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
32.2K
Stereotype Content Model02:16

Stereotype Content Model

14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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相关实验视频

Updated: Jun 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

541

一个基于标准的分类模型,使用增强和对比学习来分析不平衡的语句数据.

Junho Shin1, Jinhee Kwak1, Jaehee Jung1

  • 1Department of Information and Communication Engineering, University of Myongji, Yongin, Gyeonggi-do, South Korea.

Heliyon
|July 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了一种使用NLP的基于标准的内容分析 (CBCA) 的客观模型,大大减少了受害者陈述分析中的人类主观性,以提高法律准确性.

关键词:
基于标准的内容分析.数据增强数据增强双重对比学习学习这是一个SMBO优化器.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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科学领域:

  • 法医心理学 法医心理学
  • 计算语言学 计算语言学
  • 人工智能的人工智能

背景情况:

  • 基于标准的内容分析 (CBCA) 对于评估受害者的陈述真实性至关重要.
  • 人类分析中的主观性可能会影响证词评估的可靠性.
  • 现有的CBCA方法面临着数据分布不平衡的挑战.

研究的目的:

  • 为CBCA语句分析开发一个客观的,基于自然语言处理 (NLP) 的分类模型.
  • 通过最小化人类主观性来提高CBCA的准确性和可靠性.
  • 解决标准分类中的数据不平衡问题.

主要方法:

  • 利用NLP技术,为CBCA创建一个客观的分类模型.
  • 采用数据增强和双对比学习来微调罗伯塔语言模型.
  • 应用基于模型的优化,用于超参数调整,以最大限度地提高分类性能.

主要成果:

  • 在宏观F1比人类分类得分提高了8.5%.
  • 与以前的基准标准相比,宏观F1得分提高了24%,准确性增加了13%.
  • 该模型有效地减少了语句分析中的人类主观性.

结论:

  • 拟议的NLP模型提供了一种更客观,更可靠的方法来评估受害者陈述的可信性.
  • 这一进步对法律诉讼和刑事调查产生了重大影响.
  • 减少主观性可以提高判决的准确性,并支持司法公正.