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

Structural Classification of Joints01:20

Structural Classification of Joints

6.9K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
6.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

267
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
267
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

1.1K
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...
1.1K

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

DeepAEG: a model for predicting cancer drug response based on data enhancement and edge-collaborative update strategies.

BMC bioinformatics·2024
Same author

Phillyrin improves myocardial remodeling in salt-sensitive hypertensive mice by reducing endothelin1 signaling.

The Journal of pharmacy and pharmacology·2024
Same author

EPAC inhibitor suppresses angiogenesis and tumor growth of triple-negative breast cancer.

Biochimica et biophysica acta. Molecular basis of disease·2024
Same author

The protocol of enhanced recovery after cardiac surgery in adult patients: A stepped wedge cluster randomized trial.

American heart journal·2024
Same author

Comparative study of tumor<b>-</b>free laparoscopic and open surgery in the treatment of early<b>-</b>stage cervical cancer.

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences·2024
Same author

Dietary sodium acetate and sodium butyrate attenuate intestinal damage and improve lipid metabolism in juvenile largemouth bass (<i>Micropterus salmoides</i>) fed a high carbohydrate diet by reducing endoplasmic reticulum stress.

Animal nutrition (Zhongguo xu mu shou yi xue hui)·2024

相关实验视频

Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

通过语义改进和修剪优化嵌套命名实体识别的边界动态.

Yanglei Gan1, Yao Liu1, Yuxiang Cai1

  • 1University of Electronic Science and Technology of China, China.

Neural networks : the official journal of the International Neural Network Society
|November 21, 2025
PubMed
概括
此摘要是机器生成的。

语义精细化和修剪 (SRT) 通过改进跨度语义和减少噪音来改善嵌套的命名实体识别. 这种新的方法可以提高复杂的嵌套文本数据的准确性.

关键词:
边界检测检测 边界检测检测提取信息 提取信息嵌套命名实体识别 嵌套命名实体识别

相关实验视频

Last Updated: Jan 10, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

999

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
  • 计算语言学 计算语言学

背景情况:

  • 嵌套命名实体识别 (NER) 识别其他实体内的实体.
  • 目前基于跨度的方法在嵌套结构中与边界模糊性和语义细微差别作斗争.
  • 这导致在密集嵌套的环境中精度降低.

研究的目的:

  • 引入一种新的方法,即语义提炼和修剪 (SRT),以增强嵌套NER.
  • 解决现有方法关于边界模糊性和语义准确性的局限性.
  • 为了提高嵌套实体检测的精度.

主要方法:

  • 对于详细的语义跨度表示,SRT使用双精细的注意力机制.
  • 一个边界意识的语义精细化模块 (BSRM) 使用卷积内核对细粒度的语义差异进行精细化.
  • 一个边界修剪模块 (BTM) 通过双通道架构来减少噪音,用于语义改进和恢复.

主要成果:

  • 在嵌套的NER基准 (ACE04,ACE05,GENIA) 上,SRT实现了最先进的性能.
  • 该方法显示了对嵌套实体检测的精度的显著改进.
  • 还评估了平面NER基准 (CoNLL03) 的表现.

结论:

  • 拟议的SRT方法有效地克服了嵌套NER现有的基于跨度的方法的局限性.
  • 通过解决语义边界模糊性和减少不相关的跨度噪声,SRT提高了准确性.
  • SRT代表了嵌套命名实体识别领域的重大进步.