Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Concepts and Prototypes01:24

Concepts and Prototypes

141
The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
141
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...
14.7K
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

161
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
161

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Knowledge Graph Augmented Large Language Models for Disease Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same author

Clinical Evaluation of the Revised Biological and Clinical Staging Criteria for Alzheimer Disease in China.

Neurology·2026
Same author

Why Empirical Risk Minimization Performs Well for Open Set Domain Adaptation: A Theoretical Analysis From Causal View.

IEEE transactions on neural networks and learning systems·2026
Same author

Predicting Post-Radiotherapy Epigenetic Age Acceleration From Pre-Treatment Data Using a Machine Learning Framework in Head and Neck Cancer Patients.

Cancer medicine·2026
Same journal

STORM: Exploiting Spatiotemporal Continuity for Trajectory Similarity Learning in Road Networks.

IEEE transactions on knowledge and data engineering·2026
Same journal

Hierarchical Active Learning with Label Proportions on Data Regions.

IEEE transactions on knowledge and data engineering·2025
Same journal

Data Synthesis Reinvented: Preserving Missing Patterns for Enhanced Analysis.

IEEE transactions on knowledge and data engineering·2025
Same journal

Cafe: Improved Federated Data Imputation by Leveraging Missing Data Heterogeneity.

IEEE transactions on knowledge and data engineering·2025
Same journal

A Neural Database for Answering Aggregate Queries on Incomplete Relational Data.

IEEE transactions on knowledge and data engineering·2024
Same journal

HyperMinHash: MinHash in LogLog space.

IEEE transactions on knowledge and data engineering·2024
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K

通过任务指导图表翻译生成弱监督的概念地图.

Jiaying Lu1, Xiangjue Dong2, Carl Yang1

  • 1Department of Computer Science, Emory Univeristy, Atlanta GA, 30322.

IEEE transactions on knowledge and data engineering
|February 23, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了GT-D2G,这是一种用于自动生成概念图的新框架. 该方法从文本中创建可解释的知识图,在下游任务中超越现有技术,并提供标签效率.

关键词:
概念地图的生成 概念地图的生成文件分类 文件分类 文件分类文件总结 文件总结图表翻译翻译 图表翻译监督的弱点 监督的弱点

更多相关视频

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K
Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

16.9K

相关实验视频

Last Updated: Jul 2, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.1K
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.2K
Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
12:49

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

16.9K

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
  • 知识表示 知识表示

背景情况:

  • 概念地图生成对于从文本中进行结构化知识总结至关重要.
  • 现有的方法面临局限性:无监督方法缺乏任务导向,深度学习模型需要广泛的培训数据.

研究的目的:

  • 介绍GT-D2G (基于图表翻译的文档到图表),一个自动概念图生成框架.
  • 为了满足面向任务和数据效率的概念地图生成的需求.

主要方法:

  • 利用通用的自然语言处理 (NLP) 管道来创建最初的含义丰富的图表.
  • 将这些图表转化为简洁的结构,使用下游任务标签的弱监督.
  • 使用人类评估和实体公司的案例研究.

主要成果:

  • GT-D2G生成可解释的概念图,有效地总结结构化知识.
  • 与其他概念图生成方法相比,该框架在文档分类任务中表现出卓越的性能.
  • 验证GT-D2G的标签高效学习能力和灵活的图形大小生成.

结论:

  • GT-D2G为自动概念图生成提供了一种有效和高效的方法.
  • 生成的概念图提供了有价值的,可解释的知识结构.
  • 该框架对需要结构化知识总结和标签效率学习的应用具有前景.