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相关概念视频

Attitudes01:54

Attitudes

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Attitude is our evaluation of a person, an idea, or an object. We have attitudes for many things ranging from products that we might pick up in the supermarket to people around the world to political policies. Typically, attitudes are favorable or unfavorable: positive or negative (Eagly & Chaiken, 1993). And, they have three components: an affective component (feelings), a behavioral component (the effect of the attitude on behavior), and a cognitive component (belief and knowledge;...
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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Stereotypes, Prejudice, and Discrimination02:55

Stereotypes, Prejudice, and Discrimination

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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Stereotype Content Model02:16

Stereotype Content Model

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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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Hindsight Biases01:12

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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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Conjugate Addition (1,4-Addition) vs Direct Addition (1,2-Addition)01:27

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α,β-Unsaturated carbonyl compounds with two electrophilic sites, the carbonyl carbon, and the β carbon, are susceptible to nucleophilic attack via two modes: conjugate or 1,4-addition and direct or 1,2-addition.
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Direct addition products are...
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Updated: Sep 17, 2025

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sEntIMeldCL:通过基于统一的隐性对比机制来增强明确的知识,以进行层面情绪分析.

Khwaja Mutahir Ahmad1, Qiao Liu2, Abdullah Aman Khan3

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, 610097, PR China; School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 61170, PR China.

Neural networks : the official journal of the International Neural Network Society
|June 27, 2025
PubMed
概括

这项研究介绍了sEntIMeldCL-ALSA,这是一种新的方面级情绪分析 (ALSA) 方法,通过考虑语义相关性和处理否定来提高准确性. 该模型增强了ALSA的明确知识,在基准数据集上取得了最先进的结果.

关键词:
方面术语提取 方面术语提取面向层面的情绪分析.话语的细分 话语的细分隐含的数据增强 隐含的数据增强监督的对比学习学习

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 面层情绪分析 (ALSA) 对于精细的文本理解至关重要.
  • 现有的ALSA模型经常忽视否定影响,并与多方面语义表示作斗争.
  • 目前使用单词替换的增强方法可能无法完全捕捉语义细微差别.

研究的目的:

  • 提出一个基于统一的隐性对比机制 (sEntIMeldCL-ALSA),以增强ALSA的显式知识.
  • 为了解决处理否定和在句子内表示多个方面的局限性.
  • 改进抽取方面术语和句子极性之间的语义相关性.

主要方法:

  • 开发了一个由三个模块组成的模型:面向特定的细分适配器,基于统一的隐性增强和双重对比损失 (ExImp对比模块).
  • 将显式和隐式表示集成到一个统一的语义表示中.
  • 采用了数据增强技术,专注于极性依赖的句子.

主要成果:

  • 拟议的sEntIMeldCL模型在四个基准数据集中的三个实现了最先进的性能.
  • 在餐厅数据集上,准确度显著提高,达到87.37%.
  • 展示了增强的F1分数,在MAMS数据集上有84.69%的改进.

结论:

  • sEntIMeldCL-ALSA模型有效地增强了对面层情绪分析的明确知识.
  • 提出的方法成功地捕捉了语义相关性,并改善了处理复杂的语言现象,如否定.
  • 结果证实了该模型在提取特定方面情绪方面的优越性,在关键数据集上表现优于现有方法.